1346 Research Yang Ouyang, Yuchen Wu, He Wang, and Quan Li are with the School
of Information Science and Technology, ShanghaiTech University, and Shanghai
Engineering Research Center of Intelligent Vision and Imaging, China. Quan Li
is the corresponding author. E-mail:
<EMAIL_ADDRESS>Chenyang Zhang is with the
Department of Computer Science, University of Illinois at Urbana-Champaign.
E-mail<EMAIL_ADDRESS>Furui Cheng is with the Department of Computer
Science, ETH Zürich. E-mail<EMAIL_ADDRESS>Chang Jiang, Lixia Jin,
and Yuanwu Cao are with Zhongshan Hospital Fudan University. E-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS>
# Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and
Visualization to Enrich Medical Diagnostic Learning

Yang Ouyang0009-0000-5841-7659  Yuchen Wu0009-0005-8333-4405  He
Wang0009-0003-2550-6139  Chenyang Zhang0009-0003-1116-4895  Furui
Cheng0000-0003-2329-6126
Chang Jiang  Lixia Jin  Yuanwu Cao  and Quan Li0000-0003-2249-0728

###### Abstract

Simulation-based Medical Education (SBME) has been developed as a cost-
effective means of enhancing the diagnostic skills of novice physicians and
interns, thereby mitigating the need for resource-intensive mentor-apprentice
training. However, feedback provided in most SBME is often directed towards
improving the operational proficiency of learners, rather than providing
summative medical diagnoses that result from experience and time.
Additionally, the multimodal nature of medical data during diagnosis poses
significant challenges for interns and novice physicians, including the
tendency to overlook or over-rely on data from certain modalities, and
difficulties in comprehending potential associations between modalities. To
address these challenges, we present DiagnosisAssistant, a visual analytics
system that leverages historical medical records as a proxy for multimodal
modeling and visualization to enhance the learning experience of interns and
novice physicians. The system employs elaborately designed visualizations to
explore different modality data, offer diagnostic interpretive hints based on
the constructed model, and enable comparative analyses of specific patients.
Our approach is validated through two case studies and expert interviews,
demonstrating its effectiveness in enhancing medical training.

###### keywords:

Multimodal Medical Dataset, Visual Analytics, Explainable Machine Learning.

Introduction

Hospitals have a significant responsibility in training physicians by
providing hands-on training and supervision from experienced physicians.
However, given the workload of experienced physicians, it may be impractical
for them to provide constant supervision and guidance during every diagnostic
encounter. Additionally, it may be challenging for interns and novice
physicians to assume full responsibility for diagnostic activities without
adequate preparation [11]. To address these challenges, there is a need to
transition from the conventional “see one, do one, and teach one” mode of
medical training to a “see one, practice many, and do one” approach [71]. This
shift may offer a more effective and practical mode of training, enabling
interns and novice physicians to gain valuable hands-on experience and develop
their diagnostic capabilities.

To facilitate additional practice opportunities for novice physicians and
interns without incurring a significant medical burden, Simulation-based
Medical Education (SBME) has been proposed [2, 49]. SBME is an educational
method that employs simulation tools to emulate clinical scenarios, which is
an integral component of medical training as it allows trainees to refine
their diagnostic abilities at a reasonable expense [60, 65]. Nonetheless, SBME
evaluations are often immediate and aim to enhance learners’ medical skills
and practical expertise, rather than to enhance the summative medical
diagnosis that emerges from time and experience [49]. Furthermore, in
practice, physicians must deal with health data from various sources, such as
laboratory test results, radiological images, and medical texts, to diagnose a
patient’s condition. The multimodal nature of this health data presents
additional challenges, as novices and interns may tend to overlook or
excessively rely on certain modalities due to their lack of mindset and
clinical experience. This could lead to an incomplete understanding of the
patient’s data, which could compromise the effectiveness of the learning
outcomes. To address this challenge and help novices and interns learn to
better utilize multimodal medical data for decision-making, machine learning
(ML) techniques have been employed to model data, make predictions, and serve
as additional references for teaching purposes [62, 17]. Despite this, the
current literature [64, 54] suggests that existing works primarily focus on
adapting state-of-the-art techniques to improve model performance [6, 23, 52,
1] but overlook the impact of individual modalities on the contribution to
model results. This can make it difficult for learners to comprehend the
model’s behavior and gain useful medical knowledge from it.

In this study, we aim to improve the simulation-based medical training
workflow by designing interactions between interns and novice physicians and
multimodal ML models trained from existing medical databases. To achieve this
objective, we first conducted an observational study with four physicians with
different levels of medical expertise and one data scientist to comprehend
their primary needs and concerns regarding their mentoring and learning
experiences. Based on the learning cycle [38, 74], we summarize the learning
process of the participants in three stages: clarifying the data situation and
diagnostic tasks (i.e., learning goals), observing and comprehending each
modality data (i.e., self-awareness), and reflecting on the analysis processes
(i.e., self-adjustment). Furthermore, discussions were held with data science
experts working in the hospital to discuss the application of multimodal ML
models in medicine. A multimodal model was adapted for a showcased diagnostic
task based on the multimodal data provided by the experts. Additionally, we
propose a visual analytics system named DiagnosisAssistant to assist interns
and novice physicians enhance their learning experience. The system is built
upon the complementary multimodal model described above and presents the joint
impact of various modalities with intuitive visual representations to enable
domain experts to better understand the model decision. The system also
supports users in exploring various aspects of multimodal data and conducting
comparative analyses for individual patients. The efficiency and reliability
of the approach were validated through two case studies and expert interviews.
This study makes the following contributions:

  * •

We shadow and gain insight into the “mentor-apprentice” processes between
experienced physicians and interns/novices.

  * •

We facilitate and enhance the learning experience of interns and novice
physicians by developing a visual analytics system embedded with a multimodal
model.

  * •

We demonstrate the validity and reliability of our approach through two case
studies and expert interviews.

##  1 Related Work

The related literature can be categorized into four distinct areas that
overlap with this work: simulation-based medical education, medical diagnosis
with multimodal models, post-hoc explainability techniques, and medical data
visualization.

###  1.1 Simulation-based Medical Education

Simulation technology has become central to medical education, and the
effectiveness of simulation-based learning depends on the type of simulators
used. Simulators can be classified into three categories: part-task trainers,
computer-based systems, and integrated simulators [2]. Part-task trainers
cover basic procedural skills as well as high-fidelity virtual reality
trainers with haptic feedback for complex medical procedures like surgery [29,
37, 69]. Computer-based systems have the potential to create simulated
patients or environments and provide interfaces for learners to interact with
basic science material and receive feedback [18, 58]. Integrated simulators
combine realistic manikins with computer-driven features to simulate medical
procedures and interactions [50]. There are two types of integrated
simulators: model-driven simulators (e.g., Human Patient Simulator) and
instructor-driven simulators (e.g., "Noelle" Obstetrics Simulator) [3, 47].

The majority of the cases mentioned above involve direct feedback from SBME,
which focuses on enhancing learners’ medical skills and operational
proficiency rather than improving summative medical diagnosis and learning
experiences. This study falls under the purview of computer-based systems,
which aid interns and novice physicians in enhancing their learning
experiences by constructing and revealing a multimodal model integrated into a
visual analytics system. Furthermore, the system we have developed is designed
to foster self-directed learning by emphasizing the learning cycle process
[38, 74]. This approach allows users to enhance their knowledge and skills in
a self-paced manner, enabling them to engage in a continual process of
learning and personal development.

###  1.2 Medical Diagnosis with Multimodal Models

Medical diagnosis involves utilizing heterogeneous data, including clinical
records, laboratory tests, and radiological images. Multimodal models, driven
by ML technologies, have emerged to assist in medical diagnosis by leveraging
this diverse data. These models can be classified into two types based on
their input fusion strategy: decision-level fusion and feature-level fusion
[62, 44, 30] models. Decision-level fusion models integrate probabilistic or
categorical predictions from unimodal models using techniques like averaging,
weighted voting, or majority voting to generate a final multimodal prediction
[28, 36, 72]. For instance, Huang et al. [31] used PENet and a feedforward
neural network on CT scans and electronic medical records (EMR) to detect
pulmonary embolism, while Kawahara et al. [35] employed convolutional neural
networks (CNNs) on camera images and metadata for diagnosing skin lesions.
However, decision-level fusion may lack interaction among hidden features
despite being able to handle missing patterns. On the other hand, feature-
level fusion methods extract raw data or multimodal features into a concise
and informative representation for final prediction [62, 44, 30]. For example,
Shi et al. [62] utilized autoencoders to extract features from magnetic
resonance images (MRI) and positron emission tomography (PET) for diagnosing
Alzheimer’s disease, while Chen et al. [17] proposed an Attention Mutual-
Enhance (AME) module to fuse features during the extraction phase for
diagnosing cervical spondylosis. Feature-level fusion methods, compared to
decision-level fusion, are generally better equipped to capture complex
relationships between features from different modalities.

While numerous multimodal models employ feature-level fusion techniques to
enhance performance in specific tasks, their complex and hybrid architectures
pose a significant challenge in understanding the internal workings of these
models [52]. In contrast to previous approaches, our study takes a different
direction by focusing on comparing the effectiveness of various fusion
strategies within the context of a diagnostic task. Specifically, we have
implemented a decision-level fusion model that outperforms the feature-level
model in terms of performance. Our primary research objective is to
investigate the efficacy of fusion strategies, with a particular emphasis on
the decision-level fusion approach. It is important to note that the
interpretability of feature-level fusion models is beyond the scope of this
study.

###  1.3 Post-hoc Explainability Techniques

Post-hoc interpretability involves revealing the inner workings of an ML model
after its creation, allowing it to be applied to existing models.
Interpretable methods can be classified as either model-specific or model-
agnostic, depending on their compatibility with different models [5, 53].
Model-specific methods generate interpretations tailored to specific models,
while model-agnostic methods can be applied to any model. This study focuses
on model-agnostic approaches, specifically, surrogate model-based and feature
contribution-based methods. Surrogate model-based approaches transform complex
models into interpretable approximations, such as linear models [56], tree-
based models [7], or rule-based models [39, 57], to mimic predictions of the
original “black box” model [40, 56, 25]. Feature contribution-based approaches
investigate the impact of features on model decisions, such as permutation
importance and SHAP, which measure global and local feature importance,
respectively [27, 46, 10]. Attention-based neural networks have gained
popularity but often lack clear explanations for their predictions [53, 19,
42].

In our study, we incorporate various interpretability methods to analyze
models derived from different modalities. Specifically, when dealing with the
medical image modality, it is essential to consider its unique characteristics
for accurate model interpretation. To address this, specific tools have been
developed to accommodate these distinct characteristics [32]. Within this
context, we utilize the Guided Grad-CAM approach [59], which has demonstrated
its value in multi-class settings due to its class-specific nature [32]. For
the text modality, we employ the interpretability of transformers approach
[15]. Lastly, for the indicator modality, SHAP has been utilized. By
incorporating multiple techniques, we acknowledge the ability of each approach
to effectively highlight its respective strengths and provide insights into
the transformation of different types of raw data processed through diverse
models. Our aim is to enhance the learning experience of interns and novice
physicians in medical diagnostics by utilizing multimodal modeling and
visualizing historical medical records as a proxy.

###  1.4 Medical Data Visualization

Medical data holds valuable information and vast research potential. However,
the complex nature of electronic health records, sourced from diverse origins
with heterogeneous and temporal variations [21], makes it challenging to
identify underlying patterns. Medical data visualization empowers researchers,
professionals, and even patients to explore, examine, and make informed
decisions about patient health [12]. This discussion focuses on visualizing
two primary medical data types: time series and cohorts. Time series encompass
physiological signal data (e.g., heart rate, respiratory rate, oxygen
saturation) and patient-centered event series (e.g., treatment and medication
records) [26]. Timeline-based visualizations, such as point or interval plots,
provide detailed information about physiological data [61]. Recent
advancements include ThreadState proposed by Wang et al. [73], which employs a
new glyph matrix design and Sankey plots to identify disease progression
states. Other approaches involve summarizing patient history through timelines
[61] and storylines [8]. Retrospective cohort studies [41] and visual analysis
of longitudinal cohort data [9] have been valuable for various purposes, such
as assessing healthcare team performance [48], summarizing disease states
[73], and comparing medical image attributes [14].

Several studies have explored multimodal medical data visualization. For
example, Raidou et al. [55] integrated multiple visualization techniques,
including timeline, scatterplot, heatmap, brushing and linking, and
interactive filters, to explore radiation-induced bladder toxicity in a cohort
study using clinical data such as treatment events and patient demographics.
Mörth et al. [51] integrated clinical and radiological data, employing
heatmaps, boxplots, scatterplots, and decision trees to facilitate the
exploration of multiparametric studies for radiomic tumor profiling. Sugathan
et al. [68] introduced a longitudinal visualization approach for examining
multiple sclerosis lesions over time, enabling both qualitative and
quantitative analysis of lesion progression.

Unlike previous studies that primarily relied on specialized visualizations
for depicting multimodal data, our approach stands out by providing visual
explanations that comprehensively explore the influence of multimodal
characteristics. Moreover, our research goes a step further by creating
visualizations encompassing various data modalities to assist interns and
novice physicians in effectively navigating medical data, spanning from
patient cohorts to individual patients.

##  2 Observational Study

###  2.1 About the Team and the Mentoring Process

To gain a deeper understanding of customary procedures within the medical
diagnostic process, as well as the learning experiences of novice physicians
and interns, we collaborated with a team of five domain experts from a
reputable local hospital. The team includes two interns (I1, male; I2,
female), two chief physicians (E3, male; E4, female), and one data scientist
(E5, male) responsible for medical data engineering within the hospital.
Notably, E3 served as an experienced physician and mentor to both I1 and I2.

In the medical field, the relationship between novice physicians, interns, and
their supervisors follows a customary “mentor-apprentice” dynamic. Novice
physicians and interns are always under the direct supervision of experienced
residents or attending physicians. The medical prescriptions written by
interns and novice physicians undergo verification and endorsement by their
supervising physicians, including documentation of the courses of action they
have proposed. Ultimately, the attending physician assumes responsibility for
the patient’s care and oversees the decisions made by the interns and novice
physicians. The interns and novices are required to report their analyses to
their supervising physicians, who engage in comprehensive discussions based on
the provided information and the best available evidence. The process of
diagnosing a patient from multiple data modalities typically follows a top-
down approach. Initially, patient information is gathered from various
sources, each offering a different perspective. Physicians then review the
data from all modalities to gain an overview and carefully examine any
abnormalities detected in each modality. Based on these observations,
physicians form potential diagnoses for each data modality. In essence,
information is shared, and the control over diagnosis and treatment becomes
more comprehensive in theory.

###  2.2 Experts’ Concerns and Bottlenecks

Despite receiving standard medical training, interns and novice physicians
encounter various challenges during the practical learning process, leading to
potential issues.

Communication barriers may arise due to a shortage of doctors in the
healthcare system, particularly in primary and county hospitals, for various
reasons. “In certain situations, interns and inexperienced physicians may not
have sufficient time or opportunities to thoroughly discuss the medical
diagnostic decision-making process with their colleagues,” said E3. This lack
of communication may lead to errors and hurt patient outcomes. I1 further
highlighted that in real diagnostic scenarios, interns might refrain from
asking questions, which could also impede their learning process. “Sometimes I
want to ask a question, but given that this is a real medical diagnostic
scenario, I choose not to ask the question,” said I1.

Experienced physicians and interns/novices exhibit distinct mindsets, and
relying solely on one’s experience can be a “double-edged sword”.
Interns/novices with limited experience are more inclined to consult textbooks
and embrace challenges. As recounted by I1, an experienced physician had
initially diagnosed a patient with asthma, but the patient’s condition did not
improve despite multiple treatment attempts. However, an intern scrutinized
the patient’s physical examination data and identified the trigeminal sign
(i.e., depressions in the supraclavicular fossa, and intercostal space during
inspiration), indicating the possibility of airway stenosis. The intern’s
observations were confirmed by their supervisor, who then ordered pulmonary
function tests, leading to a correct diagnosis. This case underscores the
significance of obtaining patient information from various sources and
recognizing potential links between different modalities in order to improve
overall diagnostic accuracy.

Interns and novice physicians often lack opportunities to practice their
skills on actual patients. E4 conducted a survey of $11$ patients, and while
four were willing to be seen by an intern for minor illnesses, seven were
apprehensive and expressed concerns about misdiagnosis due to the intern’s
lack of experience, “reluctance because the intern is too young and
inexperienced and afraid of misdiagnosis.” However, there were also patients
who recognized the importance of interns having opportunities to gain clinical
experience. E3 acknowledged that while patients have the right to refuse care
from interns, “if most patients refuse, it could hinder the growth and
development of interns, leading to a discontinuity in the quality of doctors
in the future”. To streamline their learning process, a common approach in
SBME is a standardized test [33]. However, such tests may limit learners’
critical and creative thinking abilities as they focus on reference answers.
For example, “how would a physician judge if there were only two specific data
modalities” and “whether a different diagnosis would occur when certain data
modalities change.” These issues frequently arise in real clinical scenarios
where physicians must often make judgments without all necessary data
modalities simultaneously available. Thus, there is a pressing need to develop
a medical diagnostic platform to enhance the learning experiences of interns
and novice physicians.

Figure 1: The processing of medical data in different forms is conducted by
the back-end engine. It uses a multimodal model to make predictions and
employs interpretability techniques to explain them. The front-end
visualization facilitates interactive exploration for improved diagnostic
learning.

###  2.3 Experts’ Needs and Expectations

Following interviews with all experts, we compiled a list of requirements to
improve the medical diagnostic learning experience for interns and novices by
addressing potential obstacles and concerns. Our system is intended to be
consistent with the learning cycle principles, allowing users to achieve their
learning objectives, enhance self-awareness, and engage in self-adjustment.
First, learning objectives for our system should comprise of clarifying
medical diagnostic tasks and comprehending the general data situation (R.1).
Second, to develop self-awareness, visual cues for each modality data and
simple observation and comprehension of data are necessary (R.2 – R.5). Third,
it is crucial to encourage users to reflect on their analysis processes and
compare their results to other case studies to bolster their learning
experience and deepen their understanding of the diagnostic task (R.6).

R.1 Describe the focal medical diagnostic tasks and present data statistics.
According to the experts, it is essential to provide statistics regarding the
multimodal dataset, including data quality and sources for each modality.
Additionally, a clear definition of medical diagnostic tasks should be
provided.

R.2 Develop a reliable and interpretable ML model that can capture the
diagnosis process. According to the experts, ML techniques, especially deep
learning methods, are highly effective in this regard. Data scientist E5
affirmed that their prior modeling experiments have also yielded satisfactory
outcomes. However, they emphasized that certain aspects need to be addressed
if advanced models are to be implemented in actual diagnostic scenarios.
Specifically, the model’s accuracy must be sufficiently high, and it must also
capture the diagnostic process accurately. E5 further stated that “it is
crucial to understand how the model arrived at its conclusions and whether its
decision-making process aligns with medical findings”.

R.3 Convert the diagnostic process into a user-friendly representation. Once a
reliable ML model has been developed, the focus shifts toward creating an
intuitive representation of the diagnostic process for historical cases. The
experts highlighted the importance of demonstrating the model’s functionality
in a clear and easily understandable manner. As E3 noted, conveying the
abstract experience in a user-friendly manner would be beneficial for all
involved.

R.4 Show the performance of the model and the contribution of each data
modality. Although inexperienced physicians and trainees have the potential to
develop a broader perspective that transcends their specialization, they may
fall into the trap of over-reliance on a single modality due to their limited
clinical exposure. E3 provided a case study where the negligence of clinical
cues and excessive reliance on CT imaging resulted in delayed detection of
bowel cancer in a patient for a considerable length of time. Furthermore, as
E4 highlighted, “imaging techniques may not capture all the relevant details,
and their accuracy may be affected by factors such as the angle and method of
capture”. Hence, it is essential for novice physicians and interns to
comprehend the performance of the model and the contribution of each modality
to the final diagnosis.

R.5 Reveal the relationship between different modalities. When only one type
of data is available, inexperienced medical professionals can easily make
assessments based on that single modality. However, when faced with data from
multiple modalities, they may encounter difficulties and confusion. According
to E2, this difficulty arises from a lack of proficiency in correlating
information across different modalities. It is crucial for them to establish
connections between these modalities, considering that some modalities may
present contradictory findings. For example, while a clinically recommended
modality may indicate the presence of Benign Prostatic Hyperplasia, no
abnormalities may be detected through a medical ultrasound. In such cases, a
comprehensive patient analysis, including factors like age and relevant
symptoms, should guide the diagnosis to favor the ultrasound results. It is
important to note that different diseases may require different
interpretations of the relationships between modalities.

R.6 Support comparative analysis of individual patients and maintain data
provenance. Conducting comparative analysis on typical cases of specific
diseases holds significant importance in the realm of clinical practice. “By
comparing individual patients from diverse groups, we can broaden our
knowledge and comprehension of varied disease pathologies and patient
cohorts”, said I2. This aids interns and novice practitioners in developing
their diagnostic skills and enables them to make accurate diagnoses in real-
life scenarios. Additionally, the system should maintain a log of actions to
monitor and track the comparison process. In summary, the comparative analysis
of individual patient cases plays a crucial role in enhancing diagnostic
accuracy and minimizing the risk of misdiagnosis and missed diagnoses.

##  3 DiagnosisAssistant

Based on the identified needs and requirements, we have developed a novel
visual analytics system, namely DiagnosisAssistant, to enhance the diagnostic
learning experience of interns and novice physicians. The system’s
architecture (Fig. 1) encompasses a back-end engine responsible for processing
medical data from three distinct modalities, namely indicator, text, and
image. The processed data is then fed into a reliable multimodal model that
generates predictions by leveraging the aforementioned modalities. To promote
transparency, innovative interpretability techniques are employed to elucidate
the model’s predictions. The front-end visualization empowers users to
interactively explore the processed data across the three modalities, thereby
enhancing the diagnostic learning experience for interns and novice
physicians.

###  3.1 Back-end Engine

####  3.1.1 Data

Figure 2: The system interface of DiagnosisAssistant contains (A) the User
panel, (B) the Embedding Transition View, (C) the Modality Exploration View,
and (D) the Comparison View.

DiagnosisAssistant, is designed to process heterogeneous clinical data,
construct robust models, and provide diagnostic insights based on
interpretability techniques. To evaluate the system’s performance, we
collaborate closely with physicians from a prominent local hospital and used a
real-life clinical dataset for diagnosing cervical spine disorders. The
dataset comprises $750$ patient records collected during hospital visits for
cervical spine discomfort between 2012 and 2013. The patients’ ages range from
$21$ to $82$ years, with a male-to-female ratio of $1.16:1$. Each patient has
a unique CardID that corresponds to a set of clinical records, including
demographic information, laboratory test results, magnetic resonance imaging
(MRI) images, clinical reports, and diagnostic findings. We organize the data
into three modalities, namely, the indicator modality, text modality, and
image modality. The indicator modality consists of laboratory test results and
demographic information, including gender, age, height, and weight. The
unstructured data includes clinical reports and MRI images. Prior to model
training, the dataset was carefully preprocessed and checked, resulting in 626
retained instances.

####  3.1.2 Multimodal Modeling and Unfolding

In this subsection, we first present our approach for developing a multimodal
model and subsequently outline our method for unfolding the diagnostic
insights derived from the constructed model. Specifically, the design of our
model revolves around the classification of patients into three distinct
categories, namely, normal, herniated, bulging, as suggested by domain
experts.

Table 1: Model Performance.

Modality | Model | Acc. | Recall | F1
---|---|---|---|---
Indicator | XGBoost | 72.3% | 0.79 | 0.75
Random Forest | 69.5% | 0.71 | 0.70
Text | ClinicalBERT | 88.6% | 0.90 | 0.89
BioBERT | 89.1% | 0.85 | 0.87
Image | ConvNeXt | 78.8% | 0.84 | 0.82
Swin Transformer | 75.2% | 0.79 | 0.77

We explore various machine learning techniques for modeling the data across
multiple modalities, as summarized in Table 1. Subsequently, we select three
models for each modality, XGBoost [16] for indicator data, ClinicalBERT [4]
for textural data, and ConvNeXt [45] for image data, owing to their superior
performance. We fine-tune the hyper-parameters of the three models using grid
search [22] with k-fold cross-validation [67]. The dataset is divided into
training and validation sets using a random selection method with a ratio of
75:25 for training and validation, respectively. Table 1 lists the performance
of the models on the validation set with the optimal hyper-parameter settings.

Fusion Strategy. Fusing heterogeneous information from multimodal data is a
common strategy to improve model performance [20]. In this study, we
investigate two fusion strategies: decision-level fusion and feature-level
fusion (discussed in subsection 1.2). In feature-level fusion, we concatenate
the raw indicator data with the output from the penultimate layer in the
ConvNeXt and ClinicalBERT models and then feed the concatenated features into
a $12$-head, $12$-layer transformer. In decision-level fusion, we adopt a
weighted voting strategy and employ multiclass perception to learn the weights
of each modality. As shown in Table 2, the two strategies perform similarly.
However, the decision-level fusion strategy aligns better with physicians’
diagnostic process and is more robust and scalable in the absence of
modalities. Therefore we select the decision-level fusion strategy for our
model.

Table 2: Experimental Results of Fusion Strategy.

Strategy | Acc. | Recall | F1
---|---|---|---
Feature-level | 92.1% | 0.92 | 0.92
Decision-level | 91.6% | 0.94 | 0.92

Interpretability Towards Diagnosis. To improve users’ learning experience, we
apply multiple post-hoc interpretability techniques. Specifically, Guided
Grad-CAM [59] is utilized to generate saliency maps on each MRI image and
highlight important areas. The method proposed by Chefer et al. [15] is
adopted to interpret the transformer model and highlight important words. For
the indicator-modal model, SHAP is used to determine the contribution of each
feature to a particular decision. By providing saliency maps, key text
highlighting, and feature contribution quantification, comprehensive hints
about the diagnostic focus of each case are given to the users to aid in their
diagnostic learning process. All these explanations are combined to offer
interpretability to the model’s decisions.

####  3.1.3 Embedding Generation

Our objective is to provide users with an understanding of the data
distribution and model behaviors for each modality by extracting and visually
summarizing the data embeddings. For the indicator data, we directly utilize
the raw data without additional processing due to its low dimensionality. In
the case of ClinicalBERT, which follows the architecture of the BERT model and
comprises 12 transformer layers, each layer generates 768-dimensional
embeddings that capture semantic features at various levels. To retain the
maximum amount of valid information, we employ common techniques such as
summing, averaging, and concatenating selected or all layer embeddings. We
adopt the bit-wise sum of all token embeddings as the final 768-dimensional
text representation, striking a balance between computational efficiency and
information retention. As for ConvNeXt, an optimized CNN that follows the
classic CNN architecture, we extract the output from the penultimate layer
(i.e., the input of the classifier layer) to obtain a 768-dimensional image
representation. To encompass the model’s overall understanding of the data
distribution, we define a fusion embedding by concatenating the three
embedding vectors and weighting each vector element according to its modality.

###  3.2 Front-end Visualization

To facilitate the exploration and comprehension of multimodal medical data by
junior physicians, we have developed the DiagnosisAssistant interface (Fig.
2), which comprises four distinct views. The User Panel offers users an
overview of the multimodal medical dataset and model performance, while also
providing descriptive statistics about the dataset, data quality, and sources
for each modality (R.1, R.3). The Embedding Transition View (R.2, R.4, R.5)
and the Modality Exploration View (R.3, R.4, R.5) assist users in identifying
and comparing different patient cohorts across modalities. Lastly, the
Comparison View allows for the comparative analysis of individual patients,
aiding in the improved understanding of specific diseases (R.6).

####  3.2.1 Embedding Transition View

To examine the patient collections’ features in various embedding spaces, an
embedding transition view (Fig. 2(B)) has been developed. This view connects
the fusion model embedding and the three modality data (i.e., indicators,
text, images) embedding spaces (R.2). Each patient is represented by a node,
and the connections between each projection allow users to trace the same
group of patients across different embeddings. To generate two-dimensional
projections, the t-SNE method [70] is chosen as it“reveals meaningful insights
about the data and shows superiority in generating two-dimensional projection
[43].” The users can use the lasso tool to select a group of patients, and the
system will display the distribution of the selected patients by class.
Moreover, each modality data for each patient contributes to the final model
output, where the contribution is a probability estimate for each category in
the resulting model output (R.4, R.5). To show each modality data’s
contributions to different prediction classes (three classes in the running
example), bar charts are used. The contribution value is determined by the
probability mass function (PMF) [66], which characterizes the distribution of
discrete random variables. The contribution values attached to the fusion
embedding projection are calculated as weighted sums of the three modalities.

####  3.2.2 Modality Exploration View

As each modality possesses its distinct characteristics, we propose visual
interfaces and interaction for each modality individually to present intuitive
representations (R.3, R.5).

Figure 3: The Modality Exploration View includes Indicator, Text and Image
modalities.

Indicator Modality. Based on the physicians’ suggestions, we extract $34$
pertinent indicators (R.1). These indicators are structured in a collapsible
table, where each row corresponds to one indicator. In the collapsed mode,
each row includes the indicator name, an approximate distribution of
indicators concerning the lassoed patient groups, and the mean value, thereby
enabling the user to gain an overview of the indicator modality. The overall
distribution of indicator data is exhibited using strip charts. The details
can be viewed by clicking and expanding each feature, revealing a Beeswarm
plot illustrating the data value of each instance (R.6). When the user hovers
over a point, the patient information (card-id and class) and the indicator
value are displayed (Fig. 3(A)(B)). If a point is clicked, it turns red, and
the corresponding values of all the other indicators change accordingly. A bar
chart is employed to exhibit the Shapely value (Fig. 3(C)), which initially
represents the contribution of the selected patients to different classes. If
a point in the beeswarm plot is clicked, it goes to the specific Shapely
value, where the color encodes its contribution to the predicted value (red
for positive and blue for negative). The table supports operations like
sorting by contribution and filtering by indicators, providing clinicians with
more control in the exploration (R.3).

Design Alternatives. We have evaluated four types of visualizations, namely
Histogram, Density Plot, Violin Plot, and Beeswarm Plot. It was observed that
Density and Violin plots are appropriate for representing continuous data, but
inadequate for discrete data. In contrast, histograms employ interval grouping
and frequency counting to facilitate the visualization of data distribution.
However, when it comes to depicting data density and clustering, Beeswarm
Plots have exhibited distinct advantages over histograms [24]. Furthermore,
Beeswarm Plots have been found to offer a more lucid and efficient
representation of the indicator distribution. After consultation with experts,
it was determined that the Beeswarm Plot was the most suitable option.

Text Modality. We use two representations to show the text modality. The top
part represents a boxplot demonstrating the word weight of medical text for
chosen patients. The words are presented in descending order based on their
average weight, as depicted in Fig. 3(D). The boxplot is a superior
alternative to word clouds, as it can be aligned to enable a comparative
analysis of word weights among diverse patient groups. Physicians can easily
identify high-weighted terms, which can serve as visual cues for further
exploration (R.3). The lower part of the modality consists of blocks of
medical text associated with the selected patients, arranged in ascending
order according to their card ID. The words with higher weights are presented
in a darker color, and the text block also displays the patient’s card-id and
class on the left side (Fig. 3(E)). As the expert views the text, they can
quickly identify some essential keywords, such as “protrusion” and “bulging”
in the spine dataset, which physicians are particularly sensitive to.

Image Modality. The treemap is employed to categorize images based on various
categories, facilitating a panoramic view of the gallery of selected patients.
By clicking the button shown in Fig. 3(F), the gallery can be switched between
RAW image mode and CAM (Class Activation Mapping) mode. While browsing through
the thumbnails in the gallery, if a user desires to examine a specific image,
they can click on it to view the details on a larger scale. Both RAW and CAM
information are displayed in parallel, providing users with a comprehensive
understanding of the diagnostic focus of the image modality (R.3).

####  3.2.3 Comparison View

For the purpose of clinical learning and reasoning, the Comparison View
facilitates a more detailed analysis of patient-specific similarities and
differences (R.6). Through this view, users can perform a more fine-grained
comparison of individual patients, considering both aggregated multimodal data
and detailed modality fusion. Additionally, users have the option to record
notes for future reference.

Figure 4: The Sankey diagram-based design (B) for probability fusion is less
space-efficient and more cluttered compared to our design (A). Figure 5: Case
I: E3 and E4 conducted a retrospective analysis conducted to explore the
multimodal data on cervical spine degeneration. 1 They first understood the
data situation and diagnostic tasks through the User Panel. 2 Then, the
experts utilized the Embedding Transition View to identify patient groups and
behavior across modalities. 3-6 By thoroughly analyzing inter- and intra-
modality, the experts obtained insightful results that were largely consistent
with their expectations.

Instance Comparison. To present the diverse modalities of data, each with a
unique structure for each patient, an aggregated approach is employed (R.3).
The multimodal information is organized into a collapsible table, as shown in
Fig. 2(D1). Each row within the table represents a single modality, while the
three columns provide information about the modality types and the two
selected patients. The first column lists the three modality types:
indicators, text, and images. For each modality, the second and third columns
display key information for each of the two patients (R.5). This information
includes indicator distribution and shapely value data for the indicator
modality, comparisons of medical text and their associated weights for the
text modality, and RAW images alongside their CAM modes for the image
modality. As a supplementary improvement, the information presented within the
red box in Fig. 2(D1) of the system denotes the feature that exhibits the
highest SHAP value, alongside the top three weighted textual elements for each
modality corresponding to individual patients. Moreover, upon expansion, a
scrollable roster of information encompassing all features will be made
visible. The visual encodings used in this table are consistent with those
used in the Modality Exploration View, ensuring a cohesive exploration
experience for individual patients.

Probability Fusion. The process of decision fusion for the selected patients
is depicted using hybrid visualizations (Fig. 4). On the left side, three
groups of horizontal bars illustrate the predicted probability of the selected
patient for each of the three modalities, listed from top to bottom. On the
right side, a separate group of bars displays the final prediction after
decision fusion has occurred. Each bar in a group corresponds to a diagnostic
entry within the dataset. The central pie chart provides a visual
representation of the percentage contribution of each modality to the fusion
probability at the item level. Links connect the bars of corresponding entries
in each group, while the pie and links are colored to match their source
modality, facilitating the identification of modal correspondences. When
hovering over a bar, the links connect to each bar of the current entry and
depict the contribution of each modality to the fusion probability using the
pie chart.

Design Alternatives. Initially, the Sankey diagram design was explored as a
means of visualizing the decision fusion process (Fig. 4(B)). However, it was
discovered that this design did not entirely align with the multi-modal fusion
task at hand. Linking individual modality bins and final probability bins
using Sankey links would have resulted in visual clutter. Therefore, experts
were consulted, and it was decided that a more intuitive and easier-to-
interpret display of the fusion results could be achieved through the use of
simple bar charts and pie charts.

Learning Recording. To further augment the learning experience, the system
provides users with the opportunity to document their reflections on a typical
patient or a patient with an atypical diagnosis, thereby facilitating future
reviews, as illustrated in Fig. 2(D3). This feature is complemented by expert
advice (R.6), and is expected to be of significant value. For instance,
certain categories of cervical spine degeneration, such as “herniation” and
“bulge”, can be particularly perplexing to interns and novice physicians.
Consequently, they may choose to document representative cases of each type
and leverage the system’s capabilities, alongside the guidance of experienced
physicians, to systematically explore and learn from them.

##  4 Evaluation

We evaluated the effectiveness of our system through two case studies. Case I
focused on experienced physicians, demonstrating how the DiagnosisAssistant
addressed their concerns and met their requirements. Case II explored whether
our system improved the learning experience of interns and novice physicians
in medical scenarios. Before conducting these case studies, we organized a
tutorial session with the experts involved. They were introduced to the
visualization designs, interactions, and workflow of our system. After a brief
familiarization period of approximately $10$ minutes, the experts engaged in
one-hour case studies while sharing their thoughts in a think-aloud manner. We
then conducted interviews to gather their feedback on our approach.

###  4.1 Case I: Retrospective Analysis

We collaborated with E3 – E4, who possess an average of $15$ years of
experience in the relevant field, to perform a retrospective analysis. These
physicians are well-versed in the cervical spine dataset and possess
fundamental statistical knowledge pertaining to the various patient types
across different data modalities. During the evaluation, the physicians
utilized the DiagnosisAssistant to explore the multimodal data concerning
cervical degeneration and to assess whether our system could facilitate the
learning process for junior physicians.

Clarify the data situation and diagnostic tasks. After loading the data, the
experts first referred to the user panel to understand the dataset and the
model performances (Fig. 5(1)). E4 asserted that “the user panel gives me a
general understanding of the data, a clear awareness of the diagnostic task at
hand, and the model’s performance.”

Target certain patient groups. Next, the experts referred to the Embedding
Transition View to identify specific patient groups and to comprehend the
behavior of certain patients under different modalities. By utilizing the
lasso tool on each cluster, E3 discovered that the clusters in the fusion
embedding hold significance, where each cluster corresponds to a patient
cohort with identical diagnostic outcomes (Fig. 5(2)). Furthermore, he focused
on patients predicted as “bulging”. By following the links, he observed that
the patient group clustered more effectively in the image and text modalities,
whereas the indicator modality resulted in smaller clusters.

Analyze inter- and intra-modality. “The key for junior physicians to learn the
diagnosis is to learn from the raw data of each modality, quickly locate
abnormalities in each modality, and then integrate the information with
medical knowledge,” said E3. Consequently, E3 began to investigate specific
modality data in the Modality Exploration View. He discovered that “became”
plays a significant role in the majority of herniated patients, where most
text descriptions possess phrases such as “physiological curvature became
straight, the x/x intervertebral disc was slightly narrowed.” (Fig. 5(4)). In
the image modality, E3 was pleased to note that the highlighted regions
corresponded to his knowledge in making the judgment (Fig. 5(5)). Lastly,
despite the subpar performance of the indicator modality, the indicator “Age”
was mainly distributed among individuals aged $24$-$50$ years, with a higher
SHAP value, consistent with their observation that highly active or sedentary
adults are more susceptible to cervical disc injury (Fig. 5(3)). Additionally,
the contribution of each modality to the final diagnosis was well
demonstrated: the text modality contributed the most, followed by the
indicator modality, while the image modality predicted the greatest
probability of “bulging”, albeit only slightly more than “’herniated’ (Fig.
5(6)). The text and image modalities complemented each other, and the
prediction of the indicator modality was not critical.

Takeaway. E3 and E4 acknowledged that the outcomes align with their clinical
background, showcasing the system’s ability to carry out inter- and intra-
modality analysis. Overall, the physicians concluded that the system
adequately fulfilled their demands and resolved their apprehensions, as they
remarked, “it holds the promise to facilitate the learning process of novice
physicians”.

###  4.2 Case II: Learning From Demonstration

Our study involved working with I1, I2, who have two years of academic
experience but limited clinical exposure. The main objective was to utilize
our system for their educational development by learning from clinical
records. During the regulatory training phase, they were mentored by E3 – E4
and received instruction on the fundamental aspects of diagnosing cervical
degeneration in an academic setting. Before introducing them to our system,
both individuals expressed a keen interest in clinical practice, prompting us
to approach this case study with a problem-solving perspective.

During the course of our case study, intern I2 posed the question, “what are
the precise clinical differences between bulging and herniated?” She
specifically sought to understand these differences in the context of MRI
scans, “I know bulging to herniated is a transition from mild to severe, but I
want to know the exact clinical differences, especially on MRI". Following our
introduction of the dataset, the interns proceeded to explore the fusion
embedding. I1 then inquired about how to identify the entries for each
cluster, which prompted us to remind him of the lasso operation. The interns
then proceeded to lasso multiple groups of cases in each cluster, paying
particular attention to those with bulging and herniated entries. They then
utilized the modality exploration view to examine interpretability-aided
records. By selecting the “Sort by SHAP” button (Fig. 6(1)), I2 discovered
that “Age” and “Blood glucose” consistently appeared in the top three
indicators (Fig. 6(2)). She hypothesized that this finding could be attributed
to the fact that younger adults are at a higher risk for cervical
degeneration, and obesity may be a predisposing factor as indicated by blood
glucose levels, “this is unexpected, but makes sense”.

Figure 6: Case II: Learning From Demonstration. The interns explored the
Comparison View to analyze individual patient cases with CardID “80115321” and
“80145043”.

The interns further examined the bulging and herniated groups by selecting the
cases with CardID “80115321” and “80145043” and added these cases to the
Comparison View (Fig. 6(3)). They utilized the comparison table for each
modality and examined the indicator modality, noting that all indicators were
normal. They then shifted to the text modality and observed the highly
weighted words “became” in the first case and “slightly” in the second case.
Upon toggling to the image comparison table to scrutinize Raw$\&$CAM images of
both cases, the interns utilized the saliency map to identify potential lesion
locations. “The comparison tables help me a lot, especially the saliency map”,
said I1, “it’s quite surprising to see the saliency map highlights possible
lesion locations, allowing me to know where to check at first glance”. They
noted that the highlighted portion of the saliency map (Fig. 6(4))
corresponded to the area where the discs (horizontal dark stripes in the Raw
image) protruded backward in both cases (Fig. 6(5)).

I2 expressed her curiosity about the differences in radiographic detail and
commented that “both cases had discs protrusion, which I would expect, but
what makes the two protrusions different diagnoses?” (Fig. 6(6)). To get an
answer, they meticulously compared the two Raw images. They ultimately
discovered that the protrusion in the bulging case did not extend beyond the
vertical white line on the right side, whereas it did in the herniated case.
The interns then returned to the modality exploration view to confirm their
understanding, ultimately concluding that the diagnostic basis likely lies in
whether the protrusion breaks through the tissue on the right. In the
Probability Fusion View (Fig. 6(7)), I2 noted that “both cases are dominated
by images and text, as shown in the pie chart.”

Verification of conclusions and clarification of doubts. After the interns’
exploration, we invited both E3 and E4, as well as I1 and I2, to confirm their
findings and address any lingering questions. E3 confirmed I1’s conclusion
that MRI and radiology reports play a crucial role in our diagnoses, while
indicators are often only briefly considered. “We do put more emphasis on MRI
and radiology reports in our diagnoses. For indicators, we usually just take a
glance”. Additionally, E3 confirmed that obese individuals are more likely to
experience cervical spine injury due to the excess force on their cervical
spine. E4 praised the intern’s observation, noting that bulging refers to the
disc protruding outward while the outer layer of the annulus remains intact,
whereas herniated refers to the nucleus spurting out through a tear in the
annulus. E4 agreed that this aligns with their clinical radiographic judgment.
In response to the doubt from I2, E4 explained, that “in addition to bulging,
the upper highlighted area of the MRI indicates ossification of the posterior
longitudinal ligament, which is typically not found in cases of cervical
degeneration”.

###  4.3 Expert Interview

We conducted a semi-structured interview with all experts for a duration of
one hour to obtain their feedback regarding our approach.

System Performance. All experts acknowledged the effectiveness of the
DiagnosisAssistant in enhancing the medical diagnosis learning experience. I1
and I2 mentioned that before the system’s implementation, learning was limited
to observing mentors. However, with our system, they can independently explore
multimodal data. E3 and E4 expressed satisfaction with the interpretability
techniques used, especially praising the CAM technique for effectively
capturing regions of interest in their diagnosis. They also expressed a desire
to use these explanations for future investigations. Moving forward, they plan
to validate the system through extended real-world usage and supplement it
with comprehensive qualitative user studies.

Visual Designs. In general, all experts confirmed that the visual
representations were intuitive and the system was easy to use. E3 remarked
that the interface provided “precisely the information required by a medical
professional for performing targeted diagnostic analyses”. I2 noted that “I am
surprised by the interface’s functionality, which allowed for multiple
modalities of patient data to be presented simultaneously and interconnected”,
stating that it was more intuitive than their usual statistical analysis
methods. I1 and I2 noted that the Comparison View was particularly useful for
recording their observations.

Suggestions. During the exploration, I2 and E3 detected discrepancies in the
data quality, such as inaccuracies in clinical notes. They proposed that the
system should automatically propose solutions to rectify such issues.
Moreover, the current methods of maintaining data quality are carried out
behind the scenes, and physicians desire to be more involved in this process.
E3 expressed that interactive tools for anomaly detection might be required to
supervise the data quality, stating that “to ensure data quality, interactive
tools for detecting and encoding any missing or incorrect information may be
indispensable”.

Feedback from External Experts. With the assistance of our collaborating
physicians, we established communication and solicited the proficiency of
three external experts specialized in the domain of orthopedics, each
possessing a professional tenure surpassing 8 years. Their primary task
encompassed the thorough assessment of our system’s comprehensive pipeline
alongside the corresponding case studies. Consequently, they offered two
insightful feedback points for our consideration. First and foremost, the
process of clinical diagnosis entails a high level of complexity and
multifaceted nature, necessitating meticulous consideration of various
symptoms, physical examinations, and test results. In line with
recommendations put forth by external experts, the incorporation of a
comprehensive range of relevant factors and clinical outcomes within an
auxiliary diagnostic system would serve to augment both accuracy and
efficiency. Second, given that physicians across different departments often
concentrate on distinct facets of a particular disease, it becomes imperative
for interns to acquire the skill of integrating their knowledge and
experiences derived from diverse backgrounds and disciplines. This integration
is essential for effectively managing the intricate nature of clinical
practice. Consequently, the integration of AI advancements into an auxiliary
diagnostic system, aimed at offering more refined and interdisciplinary
perspectives, is likely to enhance the overall comprehension of diseases and
foster expertise from multiple perspectives, particularly among interns.

##  5 Discussion and Limitation

Contributions and Effectiveness. Our approach enhances the learning experience
of novice and intern physicians in diagnostic skills by utilizing ML models
trained on historical medical data. This approach supports the analysis of
medical data across different modalities through interpretability techniques
and visual cues, facilitating the acquisition of diagnostic skills.
Additionally, our system enables the comparative analysis of individual
patients and facilitates advanced exploration of patient characteristics in
multimodal scenarios. This approach offers two key benefits: 1) senior
clinicians can save time by reducing their deep involvement in training, and
2) novice and intern physicians can learn from objective, data-driven examples
from the past, reducing the impact of mentor bias. It is important to note
that our approach does not aim to replace the crucial role of senior
clinicians in novice and intern training but provides a cost-effective
alternative to SBME tools.

Reliability of Post-hoc Explainability Techniques. Our system utilizes various
post-hoc interpretability methods for multimodal models to help users
understand model predictions and facilitate learning. However, it is important
to note that the explanations provided by the model may not always be
accurate, which could lead to incorrect decision-making. Hence, we caution
against directly relying on the system for high-stakes clinical decisions.
Evaluating the reliability of post-hoc interpretability techniques in clinical
settings is beyond the scope of this study and will be explored in future
research. Moreover, in conjunction with advancements in human-centered
algorithm design and the utilization of more nuanced decision metrics [63], we
can enhance the acceptability and adoption of AI tools among clinicians.

Generalizability and Scalability. Although the system possesses the capability
to accommodate additional clinical datasets, such as MIMIC-III [34], the
cervical spine dataset serves as the principal experimental domain for its
evaluation and testing. Our future plans involve incorporating additional
types of data, including temporal sequences of patients’ vital signs and their
genomic profiles, which will necessitate the development of new visualizations
and interpretability techniques. Presently, the system is capable of
presenting $626$ patient records, including demographic data, test results,
clinical notes, and radiological images, effectively fulfilling the needs of
physicians. However, the scalability of the system is subject to limitations
due to the substantial computational resources demanded by data processing and
interpretability algorithms, as well as the current implementation of
scatterplots within the Embedding Transition View. Furthermore, the display
capabilities for images and treemap visualizations may pose constraints on the
effective representation of a large number of elements. In the future, we aim
to improve computational efficiency by implementing parallel computations. We
also plan to enhance the performance of scatterplots by using deck.gl on
canvas, enabling real-time rendering and smooth interaction with larger
patient datasets of up to 10,000 patients. Furthermore, we will investigate
the use of hierarchical clustering and semantic zooming as potential solutions
to address scalability challenges.

Limitations. This study has limitations in terms of data quality control,
particularly for textual data, and a qualitative user study with a limited
number of experts. Managing and analyzing clinical notes poses significant
challenges in the field of medical AI. In the future, we intend to conduct
further studies to examine the impact of data quality issues on the system’s
usage. Additionally, we plan to test and enhance the system based on long-term
real-world usage. Future evaluations may explore the usability of our system
across diverse medical departments and assess its efficacy in continuously
updated deployed systems. Additionally, considering the improvement of
communication between novice clinicians and our system, there arises a
potential need for personalization in human-AI interaction [13].

##  6 Conclusion and Future Work

In this study, we introduce DiagnosisAssistant, a visual analytics system
aimed at enhancing the learning experience of interns and novice physicians
using historical medical records as a substitute. Through observations and
analysis of interactions between experienced physicians and interns/novices,
we seek to better understand the “mentor-apprentice” process. The system
incorporates inter- and intra-modality analysis to visualize multimodal data
and integrates a multimodal model into the user interface. We evaluate the
system’s effectiveness through two case studies and expert feedback.
Additionally, we propose a further exploration of reliable post-hoc
interpretability techniques for interpreting AI decisions in multimodal
clinical scenarios.

###### Acknowledgements.

This work is partially supported by the National Natural Science Foundation of
China Youth Fund under Grant No.: 82001471, the Shanghai Frontiers Science
Center of Human-centered Artificial Intelligence (ShangHAI), and Key
Laboratory of Intelligent Perception and Human-Machine Collaboration
(ShanghaiTech University), Ministry of Education.

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