# Adaptive Smooth Activation for Improved Disease Diagnosis and Organ
Segmentation from Radiology Scans

Koushik Biswas, Debesh Jha, Nikhil Kumar Tomar, Gorkem Durak, Alpay
Medetalibeyoglu,
Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Bohrani, Ulas Bagci

###### Abstract

In this study, we propose a new activation function, called Adaptive Smooth
Activation Unit (ASAU), tailored for optimized gradient propagation, thereby
enhancing the proficiency of convolutional networks in medical image analysis.
We apply this new activation function to two important and commonly used
general tasks in medical image analysis: automatic disease diagnosis and organ
segmentation in CT and MRI. Our rigorous evaluation on the RadImageNet
abdominal/pelvis (CT and MRI) dataset and Liver Tumor Segmentation Benchmark
(LiTS) 2017 demonstrates that our ASAU-integrated frameworks not only achieve
a substantial (4.80%) improvement over ReLU in classification accuracy
(disease detection) on abdominal CT and MRI but also achieves 1%-3%
improvement in dice coefficient compared to widely used activations for
‘healthy liver tissue’ segmentation. These improvements offer new baselines
for developing a diagnostic tool, particularly for complex, challenging
pathologies. The superior performance and adaptability of ASAU highlight its
potential for integration into a wide range of image classification and
segmentation tasks.

_Keywords_ Deep Learning $\cdot$ Medical Imaging $\cdot$ Segmentation $\cdot$
Activation Function

##  1 Introduction

Computer Aided Detection (Classification): Detecting pathologies from CT and
MRI scans automatically is crucial for accurate diagnosis, early detection,
and treatment planning. Abdominal CT exams are cost-effective and widely
available, making them essential for emergency conditions and detecting
lesions. They provide quick and high-resolution images that are particularly
useful for identifying acute abdominal pathologies and abdominal tumors. On
the other hand, abdominal MRI images provide superior soft tissue contrast and
are more effective in classifying tumor types without using ionizing
radiation. Automated disease detection from abdominal scans (CT, MRI) plays a
pivotal role in the rapid and accurate diagnosis of critical emergencies such
as acute pancreatitis, acute appendicitis, and various tumoral lesions
affecting organs such as the pancreas, liver, and kidneys. Missed diagnoses of
conditions like acute pancreatitis can be life-threatening, which highlights
the critical role of automated detection systems in preventing potential
mortalities.

Early detection is particularly important for conditions like precursor
pancreatic lesions, where timely identification can significantly impact
prognosis and patient outcomes. The potential occurrence of pancreatic cancer
underlines the importance of rapid intervention at an earlier stage, given the
poor prognosis associated with delayed detection. The automatic detection and
classification of liver lesions as benign or malignant is crucial for follow-
up procedures and treatment strategies. Similarly, detecting lesions in organs
like the prostate and adrenal glands helps in patient management and
prognosis. Accurately interpreting abdominal scans for these conditions is
essential for guiding healthcare professionals in making informed decisions
about treatment approaches and ensuring optimal patient care. After all,
automated disease detection in abdominal imaging not only improves diagnostic
accuracy but also has the potential to significantly enhance prognosis,
patient management, and the quality of healthcare services.

Abdominal anatomy is complex and variable, with numerous organs and structures
closely packed together. As a result, distinguishing between healthy and
pathological tissues in earlier phases can be challenging due to subtle
differences Reyes et al. (2008). This complexity can lead to variability in
interpretation among radiologists, particularly with rare or special
conditions. CAD-based algorithms can be helpful in reducing diagnostic errors
and improving diagnosis.

Organ Segmentation: For segmentation motivation, we would like to focus on the
liver organ for an example, although what we propose herein is generic and can
be applied to any organ, pathology, and non-medical applications. We choose
the liver because liver cancer is the third most common cause of cancer-
related death worldwide Arnold et al. (2020), and the liver needs to be
quantified either by volume for several diseases, and also its labelled
boundary is used for CAD systems where algorithms seek for lesions inside the
liver volume. Besides, the liver is one of the most challenging organs to
segment because of its highly variable shape and close proximity to other
organs. Liver segmentation is a prerequisite also for the localization of
tumors for targeted therapies and the detailed planning of surgical
interventions. It demands the highest levels of precision, as the outcomes can
directly influence therapeutic decisions and patient prognosis. The
variability in contrast, shape, and appearance of liver tissue poses
significant challenges. Therefore, automated methods can play an essential
role in handling such complexity.

Domain specific activation functions are needed: Current deep learning
approaches have shown promise in enhancing the quality of classification and
segmentation tasks Jha et al. (2020); Tomar et al. (2022); jha (2023).
However, they are often constrained by the limitations of the activation
functions utilized, particularly in convolutional neural networks (CNNs).
Traditional activation functions such as ReLU Nair and Hinton (2010) and its
variant (for example, Leaky ReLU Xu et al. (2015), PReLU He et al. (2015)),
despite being instrumental to the success of CNN, exhibit notable
shortcomings. They are prone to information loss in areas of negative input
and often fail to capture the subtle nuances necessary for delineating
intricate anatomical features, leading to potential inaccuracies in
segmentations, which are highly undesirable in a clinical setting. This
necessitates the development of more specialized functions tailored to the
domain-specific needs of medical image analysis.

Our proposal: Here, we present a novel smooth activation function called the
Adaptive Smooth Activation Function (ASAU), carefully designed to handle the
challenges prevalent in medical image analysis. This function embodies a
methodological shift towards smoother, more continuous transitions, offering
refined gradients that promote the intricate learning necessary for high-
fidelity classification and segmentation tasks. With the rigorous testing and
empirical evaluation on classification and segmentation tasks, our research
indicates that applying this ASAU within conventional CNN architectures can
substantially enhance the classification for multiclass disease detection from
abdominal scans and liver segmentation from CT scans. Our main contributions
are:

  * •

We have introduced a novel Adaptive Smooth Activation Function (ASAU) that
significantly enhances the classification (disease detection) and segmentation
(liver) accuracy.

  * •

Our extensive comparative analysis using Abdomen/Pelvis benchmark
classification and healthy liver segmentation datasets in both CT and MRI
modalities demonstrates almost 5% improvement in accuracy in both CT and MRI
cases and 1% improvement for segmentation tasks (for an organ such as liver,
1% is a significant increment) that show the effectiveness of ASAU.

Figure 1: Approximation of Maximum function using ASAU for varying values of
a, b, $\alpha$, and $\beta$. As $\beta\rightarrow\infty$, ASAU smoothly
approximates the maximum function.

Figure 2: Approximation of Leaky ReLU using ASAU for different values of a, b,
$\alpha$, and $\beta$. As $\beta\rightarrow\infty$, ASAU smoothly approximates
Leaky ReLU.

Figure 3: Approximation of ReLU using ASAU for different values of a, b,
$\alpha$, and $\beta$. As $\beta\rightarrow\infty$, ASAU smoothly approximates
ReLU.

##  2 Method

###  2.1 Adaptive Smooth Activation Function

We present the Adaptive Smoothing Activation Unit (ASAU) as a way of learning
to activate the neurons or not. In this paper, we show how to approximate the
maximum function using a smooth function. We consider the maximum function of
two values, $\text{max}(x_{1},x_{2})$, which is defined as follows:

$\text{max}(x_{1},x_{2})=\begin{cases}x_{1},&\text{if }x_{1}\geq x_{2}\\\
x_{2},&\text{otherwise}\end{cases}$ (1)

We can rewrite the equation (1) as follows:

$\text{max}(x_{1},x_{2})=x_{1}+\text{max}(0,x_{2}-x_{1}).$ (2)

Note that the maximum function is not differentiable at the origin, but we
need differentiable functions during backpropagation. Also, note that the
popular activation functions like ReLU Nair and Hinton (2010), Leaky ReLU Maas
et al. (2013), and Parametric ReLU are special cases of the maximum function,
and they are not differentiable at the origin. Mish Misra (2020) is a smooth
activation function that is handcrafted and fixed. If we can add a parameter
$\beta$ in Mish, it can approximate the ReLU activation function smoothly.
Note that,

$\text{if}\ \beta\rightarrow\infty,\\\ \text{then}\
xtanh(\alpha\text{SoftPlus}(\beta x))\approx max(0,x)$ (3)

where $\alpha$ is another smoothing parameter and SoftPlus is defined as
$ln(1+e^{x})$. Maxout activation Goodfellow et al. (2013) is a linear
combination of the convex functions that generalize the ReLU or its variants.
Replacing the equation (3) in equation (2), we have

$\text{max}(x_{1},x_{2})\approx
x_{1}+(x_{2}-x_{1})tanh(\alpha\text{SoftPlus}(\beta(x_{2}-x_{1}))$ (4)

If we consider $x_{1}=ax$ and $x_{2}=bx$ we can get an approximation of the
Maxout family; in particular, we can derive the smooth approximation of the
Leaky ReLU and ReLU activation function (by considering $a=1$ or $a=0$
respectively).

$\text{max}(ax,bx)\approx ax+(b-a)xtanh(\alpha\text{SoftPlus}(\beta(b-a)x)$
(5)

Figure (3) shows how the maxout has been approximated with the ASAU function.
Similarly, Figure (3) and Figure (3) show how the Leaky ReLU and ReLU are
approximated with the ASAU function, respectively.

##  3 Experiments

Table 1: Activation functions and their impact on RadImageNet 28 classes Abdominal/Pelvis CT Scans. Activation Function | Method | Macro Average | Micro Average | Accuracy | MCC
---|---|---|---|---|---
Precision | Recall | F1-score | Precision | Recall | F1-score
ReLU | ResNet-50 | 35.74 | 23.16 | 23.35 | 67.67 | 67.67 | 67.67 | 67.67 | 52.84
LReLU | ResNet-50 | 35.97 | 23.56 | 23.65 | 68.10 | 68.10 | 68.10 | 68.10 | 53.37
PReLU | ResNet-50 | 36.30 | 23.89 | 23.77 | 68.77 | 68.77 | 68.77 | 68.77 | 54.10
ASAU | ResNet-50 | 46.29 | 31.30 | 34.15 | 72.47 | 72.47 | 72.47 | 72.47 | 61.10
ReLU | ResNet-18 | 33.70 | 25.83 | 27.43 | 70.12 | 70.12 | 70.12 | 70.12 | 57.10
Leaky ReLU | ResNet-18 | 33.91 | 25.98 | 27.60 | 70.38 | 70.38 | 70.38 | 70.38 | 57.52
PReLU | ResNet-18 | 34.20 | 26.31 | 27.99 | 70.52 | 70.52 | 70.52 | 70.52 | 57.67
ASAU | ResNet-18 | 36.50 | 27.30 | 28.99 | 71.10 | 71.10 | 71.10 | 71.10 | 58.59
Table 2:  Activation functions and their impact across 26 Classes in RedImageNet Abdominal/Pelvis MRI Scans. Activation Function | Method | Macro Average | Micro Average | Accuracy | MCC
---|---|---|---|---|---
Precision | Recall | F1-score | Precision | Recall | F1-score
ReLU | ResNet-50 | 40.43 | 24.83 | 28.04 | 86.86 | 86.86 | 86.86 | 86.86 | 62.92
LReLU | ResNet-50 | 40.56 | 24.99 | 28.56 | 87.10 | 87.10 | 87.10 | 87.10 | 63.25
PReLU | ResNet-50 | 41.10 | 25.20 | 28.81 | 87.25 | 87.25 | 87.25 | 87.25 | 63.47
ASAU | ResNet-50 | 44.46 | 33.23 | 36.17 | 89.20 | 89.20 | 89.20 | 89.20 | 69.75
ReLU | ResNet-18 | 35.15 | 26.66 | 28.82 | 87.96 | 87.96 | 87.96 | 87.96 | 66.65
LReLU | ResNet-18 | 35.67 | 26.87 | 28.99 | 88.12 | 88.12 | 88.12 | 88.12 | 66.72
PReLU | ResNet-18 | 35.91 | 26.80 | 29.10 | 88.27 | 88.27 | 88.27 | 88.27 | 66.81
ASAU | ResNet-18 | 38.58 | 27.58 | 29.67 | 88.62 | 88.62 | 88.62 | 88.62 | 67.32
Table 3: Comparison of different activation functions on liver segmentation benchmark (LiTS) dataset. Activation Function | UNet Ronneberger et al. (2015) | DoubleUNet Jha et al. (2020) | ColonSegNet Jha et al. (2021a) | UNext Valanarasu and Patel (2022)
---|---|---|---|---
mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec.
ReLU | 82.06 | 73.40 | 77.82 | 91.10 | 86.24 | 77.89 | 80.68 | 95.00 | 80.87 | 71.71 | 80.07 | 87.50 | 80.27 | 71.31 | 78.84 | 87.82
LReLU | 82.47 | 73.98 | 78.46 | 91.17 | 86.27 | 78.21 | 80.20 | 95.67 | 80.84 | 71.69 | 79.31 | 88.10 | 80.92 | 72.15 | 76.84 | 90.85
PReLU | 82.71 | 73.91 | 78.77 | 91.21 | 86.39 | 78.09 | 80.39 | 95.59 | 78.91 | 69.85 | 77.89 | 86.27 | 79.08 | 69.95 | 77.38 | 88.55
ASAU | 83.62 | 75.08 | 80.59 | 93.29 | 86.88 | 78.73 | 80.77 | 96.58 | 83.03 | 74.18 | 78.79 | 91.17 | 81.98 | 73.03 | 79.01 | 90.45
Table 4: Comparison of different activation functions on liver tumor segmentation benchmark (LiTS) Benchmark dataset. Activation Function | TransNetR jha (2023) | TransResUNet Tomar et al. (2022) | ResUNet++ Jha et al. (2019) | NanoNet-A Jha et al. (2021b)
---|---|---|---|---
mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec. | mDSC | mIoU | Rec. | Prec.
ReLU | 86.11 | 77.95 | 80.16 | 96.34 | 86.38 | 78.23 | 80.85 | 95.77 | 77.03 | 68.19 | 79.90 | 83.57 | 75.05 | 66.53 | 73.33 | 83.25
LReLU | 86.30 | 78.37 | 80.37 | 96.67 | 86.18 | 77.82 | 79.80 | 96.59 | 75.03 | 66.35 | 71.64 | 84.05 | 74.05 | 65.02 | 73.36 | 84.23
PReLU | 86.39 | 78.29 | 80.32 | 96.52 | 85.68 | 77.30 | 79.46 | 96.12 | 74.39 | 66.02 | 74.38 | 81.99 | 74.84 | 66.08 | 72.47 | 85.24
ASAU | 86.41 | 78.49 | 80.30 | 96.65 | 86.35 | 78.15 | 79.89 | 96.67 | 78.12 | 69.56 | 81.50 | 83.81 | 76.57 | 67.30 | 75.88 | 84.61

###  3.1 Datasets

For the classification tasks, we use RadImageNet Mei et al. (2022) database,
which is an open-access medical imaging database designed to improve transfer
learning performance on downstream medical imaging applications and perhaps
the largest ever medical imaging dataset so far. From the whole dataset, we
experiment on CT abdominal/pelvis, consisting of 28 disease classes with an
average class size of 4994 and a total of 139,825 slices (i.e., the dataset is
designed to have slices per disease although the scans are volumes-3D). The CT
dataset contains 28 (disease) classes such as adrenal pathology, arterial
pathology, ascites, bil dil, bladder pathology, bowel abnormality, bowel
inflammation, bowel mass degenerative changes, dilated urinary tract, fat-
containing tumor, gallbladder pathology, gallstone, intraperitoneal mass,
liver lesion, normal osseous, neoplasm, ovarian pathology, pancreatic lesion,
post-op, prostate lesion, renal lesion, soft tissue collection, soft tissue
mass, splenic lesion, urolithiasis and uterine pathology. We also experimented
on the MRI abdomen/pelvis dataset consisting of 26 distinct (disease) classes
with an average class size of 3513 slices and a total number of 91,348 slices.
While most of these classes overlap with the CT dataset, the MRI dataset
uniquely includes classes like enlarged organs and liver disease, which are
not in the CT dataset. The CT dataset, conversely, has a specific class for
entire abdominal organs.

For the segmentation tasks, we select the Liver Tumor Segmentation Benchmark
(LiTS) Bilic et al. (2023) dataset, which is a multi-center dataset collected
from seven clinical centers. It contains 201 CT images of the abdomen. The
dataset is completely anonymized, and the images have been reviewed visually
to preclude the presence of personal identifiers. The whole dataset is
distributed into a training dataset with 130 CT scans, and the test dataset
has 71 CT scans. Only the training dataset is made publically available. Thus,
we trained the dataset for our experimentation.

###  3.2 Implementation details

We used the PyTorch Paszke et al. (2019) framework for all the segmentation
experiments. The networks were configured to train for liver segmentation
tasks with a batch size of 16 and a learning rate set to 1e-4. 500 epochs of
training were performed to fine-tune the network parameters adequately with an
early stopping patience of 50. To enhance the performance of our network, we
used a combination of binary cross-entropy and dice loss, and an Adam
optimizer was chosen for parameter updates. The data was split into 80% for
training, 10% for validation, and 10% for testing. We resized the image to
$256\times 256$ pixels in-plane resolution to optimize the trade-off between
training time and model complexity. To avoid bias, we also split the cases
into independent training (70 patients), validation (30 patients), and test
(30 patients) sets. The volumetric CT scans were processed pseudo-3D (slice-
by-slice) to fit into regular computer hardware (GPU). During prepossessing,
we extracted the healthy liver masks.

For classification (disease detection) experiments, we consider Tensorflow-
Keras Chollet et al. (2015) framework. We consider ResNet-18 He et al. (2016a)
and ResNet-50 He et al. (2016a) as our baseline classification networks. The
networks are trained with batch size 32, initial learning rate 0.00001 with
Adam Kingma and Ba (2017) optimizer and $1e^{-4}$ weight decay rate. The data
was split into 80% for training, 10% for validation, and 10% for testing.
Results are reported on CT scan image data in Table 1 and MRI image data in
Table 2.

###  3.3 Results and Discussion

Table 1 shows the results of ResNet-50 He et al. (2016b) and ResNet-18
architectures on the CT abdominal/pelvis scan dataset. Here, we examine the
efficacy of ReLU, LReLU, and PReLU activation along with our proposed ASAU.
For all the experiments, the ASAU-based method outperformed all other
experimental settings in all the metrics with a significant margin. On the
ResNet-50-based architecture, the ASAU integrated method obtains a high
improvement of 8.26% in terms of MCC. Table 2 shows the MRI abdominal/pelvis
dataset results. Again, here, ASU-integrated ResNet50 obtained an MCC score of
69.75%, which is 6.83% better than ReLU based method.

Table 3 and Table 4 show LiTs datasets’ results. Here, we have compared the
performance of eight state-of-the-art medical image segmentation methods (UNet
Ronneberger et al. (2015), DoubleUNet Jha et al. (2020), ColonSegNet Jha et
al. (2021a), UNext Valanarasu and Patel (2022), TransNetR jha (2023),
TransResNet Tomar et al. (2022), ResUNet++ Jha et al. (2019) and NanoNet-A Jha
et al. (2021b)) with four different activation functions. From the table, it
can be observed that ASAU has significant improvement from 1% to 3% as
compared to the ReLU activation function. DoubleUNet, along with ASAU, has set
a new baseline for liver segmentation tasks with a high dice score of 86.88%,
mIoU of 78.73%, recall of 80.77%, and precision of 96.58%.

##  4 Conclusion

Our study introduces a pioneering activation function, Adaptive Smooth Maximum
Unit (ASAU), to enhance the classification of abdominal organs and segment
abdominal liver CT images. The promising results attained by our framework on
the RadImageNet and LiTS datasets underscore its potential applicability to a
broader range of organ imaging tasks. Future research endeavors are directed
towards harnessing three-dimensional spatial information more effectively,
aiming to refine the diagnostic tools available to clinicians further and
potentially extend the application of our method to include a comprehensive
suite of abdominal organs, enhancing our approach’s overall robustness and
generalization.

### Compliance with Ethical Standards

This research study was conducted retrospectively using human subject data
made available in open access by. Ethical approval was not required, as
confirmed by the license attached with the open-access data.

### Conflicts of Interest

The authors have no relevant financial interests to disclose.

## References

  * Reyes et al. [2008] Mauricio Reyes, Miguel A Gonzalez Ballester, Zhixi Li, Nina Kozic, Ronald M Summers, and Marius George Linguraru.  Interpretability of anatomical variability analysis of abdominal organs via clusterization of decomposition modes.  In _Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society_ , pages 355–358, 2008.
  * Arnold et al. [2020] Melina Arnold, Christian C Abnet, Rachel E Neale, Jerome Vignat, Edward L Giovannucci, Katherine A McGlynn, and Freddie Bray.  Global burden of 5 major types of gastrointestinal cancer.  _Gastroenterology_ , 159(1):335–349, 2020.
  * Jha et al. [2020] Debesh Jha, Michael A Riegler, Dag Johansen, Pål Halvorsen, and Håvard D Johansen.  DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation.  In _Proceedings of the IEEE 33rd International Symposium on computer-based medical systems (CBMS)_ , pages 558–564, 2020.
  * Tomar et al. [2022] Nikhil Kumar Tomar, Annie Shergill, Brandon Rieders, Ulas Bagci, and Debesh Jha.  TransResU-Net: Transformer based ResU-Net for real-time colonoscopy polyp segmentation.  _arXiv preprint arXiv:2206.08985_ , 2022.
  * jha [2023] _TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing_ , 2023.
  * Nair and Hinton [2010] Vinod Nair and Geoffrey E Hinton.  Rectified linear units improve restricted boltzmann machines.  In _Proceedings of the 27th international conference on machine learning (ICML-10)_ , pages 807–814, 2010.
  * Xu et al. [2015] Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li.  Empirical evaluation of rectified activations in convolutional network.  _arXiv preprint:1505.00853_ , 2015.
  * He et al. [2015] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.  In _Proceedings of the IEEE international conference on computer vision_ , pages 1026–1034, 2015.
  * Maas et al. [2013] Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al.  Rectifier nonlinearities improve neural network acoustic models.  In _Proc. icml_ , volume 30, page 3, 2013.
  * Misra [2020] Diganta Misra.  Mish: A self regularized non-monotonic activation function, 2020.
  * Goodfellow et al. [2013] Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio.  Maxout networks, 2013.
  * Ronneberger et al. [2015] Olaf Ronneberger, Philipp Fischer, and Thomas Brox.  U-Net: Convolutional Networks for Biomedical Image Segmentation.  In _Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention_ , pages 234–241, 2015.
  * Jha et al. [2021a] Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D Johansen, Dag Johansen, Jens Rittscher, Michael A Riegler, and Pål Halvorsen.  Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy using Deep Learning.  _IEEE Access_ , 9:40496–40510, 2021a.
  * Valanarasu and Patel [2022] Jeya Maria Jose Valanarasu and Vishal M. Patel.  Unext: Mlp-based rapid medical image segmentation network, 2022.
  * Jha et al. [2019] Debesh Jha, Pia H Smedsrud, Michael A Riegler, Dag Johansen, Thomas De Lange, Pål Halvorsen, and Håvard D Johansen.  ResUNet++: An Advanced Architecture for Medical Image Segmentation.  In _Proc. of int. symposium on multimedia_ , pages 225–2255, 2019.
  * Jha et al. [2021b] Debesh Jha, Nikhil Kumar Tomar, Sharib Ali, Michael A Riegler, Håvard D Johansen, Dag Johansen, Thomas de Lange, and Pål Halvorsen.  Nanonet: Real-time polyp segmentation in video capsule endoscopy and colonoscopy.  In _Proceedings of the International Symposium on Computer-Based Medical Systems (CBMS)_ , pages 37–43, 2021b.
  * Mei et al. [2022] Xueyan Mei, Zelong Liu, Philip M Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine E Link, Thomas Yang, et al.  RadImageNet: RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning.  _Radiology: Artificial Intelligence_ , 4(5):e210315, 2022.
  * Bilic et al. [2023] Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, et al.  The liver tumor segmentation benchmark (lits).  _Medical Image Analysis_ , 84:102680, 2023.
  * Paszke et al. [2019] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al.  Pytorch: An imperative style, high-performance deep learning library.  _Advances in neural information processing systems_ , 32, 2019.
  * Chollet et al. [2015] François Chollet et al.  Keras.  https://keras.io, 2015.
  * He et al. [2016a] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In _Proceedings of the IEEE conference on computer vision and pattern recognition_ , pages 770–778, 2016a.
  * Kingma and Ba [2017] Diederik P. Kingma and Jimmy Ba.  Adam: A method for stochastic optimization, 2017.
  * He et al. [2016b] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In _Proceedings of the IEEE conference on computer vision and pattern recognition_ , pages 770–778, 2016b.