# Boosting Medical Image Segmentation Performance with Adaptive Convolution
Layer

Seyed M.R. Modaresi
LIPN-UMR-CNRS 7030,
Sorbonne University Paris Nord,
Paris, France
<EMAIL_ADDRESS>
&Aomar Osmani
LIPN-UMR-CNRS 7030,
Sorbonne University Paris Nord,
Paris, France
<EMAIL_ADDRESS>
&Mohammadreza Razzazi
Computer Engineering Department,
Amirkabir University of Technology,
Tehran, Iran
<EMAIL_ADDRESS>
Abdelghani Chibani
Laboratory of Images, Signals and Intelligent Systems
Université Paris-Est Créteil,
Paris, France
<EMAIL_ADDRESS>
###### Abstract

Medical image segmentation plays a vital role in various clinical
applications, enabling accurate delineation and analysis of anatomical
structures or pathological regions. Traditional convolutional neural networks
(CNNs)have achieved remarkable success in this field. However, they often rely
on fixed kernel sizes, which can limit their performance and adaptability in
medical images where features exhibit diverse scales and configurations due to
variability in equipment, target sizes, and expert interpretations. In this
paper, we propose an adaptive layer placed ahead of leading deep-learning
models such as UCTransNet, which dynamically adjusts the kernel size based on
the local context of the input image. By adaptively capturing and fusing
features at multiple scales, our approach enhances the network’s ability to
handle diverse anatomical structures and subtle image details, even for
recently performing architectures that internally implement intra-scale
modules, such as UCTransnet. Extensive experiments are conducted on benchmark
medical image datasets to evaluate the effectiveness of our proposal. It
consistently outperforms traditional CNNswith fixed kernel sizes with a
similar number of parameters, achieving superior segmentation Accuracy, Dice,
and IoU in popular datasets such as SegPC2021 and ISIC2018. The model and data
are published in the open-source repository, ensuring transparency and
reproducibility of our promising results.

Boosting Medical Image Segmentation Performance with Adaptive Convolution
Layer

_Keywords_ Medical Image Segmentation $\cdot$ Deep Learning $\cdot$ Adaptive
Convolution

##  1 Introduction

As a trending subject in the field of image processing and computer vision
(Asgari Taghanaki et al., 2021), Medical image segmentation (MIS)involves
extracting the boundaries of desired targets, such as tumors, in medical
images and determining their class (Luo et al., 2022). The accurate
segmentation of medical images, such as Computed Tomography (CT), Positron-
Emission Tomography (PET), and Magnetic Resonance Imaging (MRI), plays a vital
role in the diagnosis and treatment of various diseases and assists physicians
in patient management, including staging, assessment, and prognosis of the
treatment response (Li et al., 2019; Liu et al., 2021; Tian et al., 2021).

Deep learning-based MIShas gained considerable traction in recent years (Chen
et al., 2023; Houssein et al., 2021; Devunooru et al., 2021; Asgari Taghanaki
et al., 2021; Malhotra et al., 2022; Luo et al., 2022; Kaviani et al., 2022).
A myriad of models has been introduced in the literature for various MIStasks
and clinical outcomes, encompassing multi-organ detection, tissue mass
detection, tumor or nodule segmentation and classification, cell counting,
multiple diagnoses, prognosis, and the prediction of treatment outcomes for
various chronic diseases like cancers or neurodegenerative diseases (Kumar et
al., 2022; Devunooru et al., 2021; Roy et al., 2023; Simpson et al., 2019;
Antonelli et al., 2022; Roth et al., 2016; Song et al., 2022). In the context
of cancer diagnosis, for instance, deep models such as (Melekoodappattu et
al., 2022; Eali et al., 2022; Iqbal and Qureshi, 2022; Isensee et al., 2021;
Singh and Alam, 2022) have shown improved performance in segmenting tumors or
nodules. Despite the significant progress, implementing deep learning in
MIScontinues to pose challenges since medical images often contain noise,
artifacts, adhesions, and other distortions that can negatively affect the
performance of deep learning models, particularly when discerning tumoral
tissue boundaries and surroundings (Kumar et al., 2022; Malhotra et al., 2022;
Tian et al., 2021).

CNNmodels are widely used for analyzing and processing medical images. The
fundamental block of CNNis the convolution layer, which contains tiny
adjustable weight grids (known as kernels) that convolved on the input image.
This work involves moving the kernel over the image, and at every stop, it
performs a mathematical operation (dot product) using the weights of filters
and the pixel values of the image to get a single output. This process
provides Local Receptive Fields for every pixel, assisting the model in
concentrating on nearby characteristics. As we go deeper, the model starts
recognizing more intricate and broader patterns, enhancing its understanding
of the image (Liu et al., 2021).

Typically, kernels of a predetermined size are employed, often configured as a
3$\times$3 grid. This can be likened to a windowing technique where the kernel
moves over the image in small segments, processing each segment to create a
corresponding output in a new feature map. This process involves the kernel
striding across the image, analyzing one small region at a time. A number of
research works highlight that the size of these receptive fields plays a
crucial role in enhancing the efficiency of the models (Ibtehaz and Rahman,
2020; Milletari et al., 2016; Wang et al., 2021a; Qiu et al., 2018).
Consequently, sticking to a kernel of a fixed size might not work well for
every image. For instance, larger input images or targets might require bigger
receptive fields. As demonstrated in figure 1, having varying scales in
different images suggests the usefulness of having diverse receptive fields to
accommodate all of them (Ibtehaz and Rahman, 2020).

Figure 1: Illustration of diverse scales observed in medical images sourced
from the SegPC-2021 dataset. The green contours highlight the regions
containing cancerous lesions.

To address these challenges, this paper focuses on developing an adaptive
layer to be placed ahead of leading deep models, such as UCTransNet
architecture for MIS. The proposed model aims to overcome the limitations
imposed by fixed kernel sizes in traditional CNNsin the first layer, which may
not adequately handle diverse anatomical structures, irregular shapes, and
varying feature scales in medical images. It dynamically adjusts the kernel
size based on the local context of the input image. This adaptive mechanism
enables the network to capture relevant multiple scale features from the
original input image before feeding to the inner network, leading to improved
segmentation accuracy and robustness.

In the experiments, we conduct an extensive evaluation of this architecture
using benchmark medical image datasets (ISIC2018 and SegPC2021 described in
section 4.2). By comparing the results with traditional CNNsthat employ fixed
kernel sizes, we demonstrate the superior performance and generalizability of
our adaptive approach. To ensure transparency, reproducibility, and
consistency in implementation, our method and used datasets, including the
state-of-the-art deep models, are published in our open-source
repository111https://github.com/modaresimr/adaptive_mis.

The rest of the paper is organized as follows: Section 2 provides an overview
of the related work in medical image segmentation and mutli-scale CNN
architectures. Section 3 describes the methodology and architecture of the
proposed architecture in detail. Section 4 presents the experimental setup,
including the datasets, evaluation metrics, the results and analysis, followed
by the conclusion and directions for future research in Section 5.

##  2 Related Works

The well-known U-Net model proposed by Ronneberger et al. (2015) gained
significant attention and is an influential architecture in the field of deep
learning. Similar to the Autoencoder models, the U-Net model contains the
encoder (contracting) and the decoder (expanding) paths. The unique feature of
U-Net is the incorporation of skip connections that enable the flow of
information from the encoder to the decoder at different scales, facilitating
the preservation of spatial details and improving the localization accuracy of
the segmentation results (Ronneberger et al., 2015; Azad et al., 2022).
Numerous extensions of U-Net have been proposed to improve recognition quality
in medical tasks. Azad et al. (2022) provide a comprehensive survey on U-Net
and categorize the U-Net extensions into Skip Connection Enhancements,
Backbone Design Enhancements, Bottleneck Enhancements, Transformers, Rich
Representation Enhancements, and Probabilistic Design. The MISSFormer model
(Huang et al., 2023) redesigns the U-Net architecture by incorporating a
position-free and hierarchical U-shaped transformer. It utilizes the Enhanced
multi-scale Transformer module to bridge the gap between the encoder and
decoder feature maps. It has a slightly higher performance in the Synapse
dataset (Azad et al., 2022).

The recent UCTransNet (Wang et al., 2022) proposes replacing simple skip
connections in U-Net with a multi-scale channel-wise module to solve the
semantic gaps for an accurate MIS. It also includes an attention mechanism and
transformer sub-module. The attention mechanism implicitly learns to suppress
irrelevant regions while emphasizing the regions of interest, while the
transformer aids in capturing long-range dependencies and addresses the
limitation of local receptive fields. The transformer sub-module tokenizes
feature maps in each stage within the appropriate patch sizes.

In addition to altering skip connections, transformers, and attention
mechanisms, alternative backbones are commonly used to improve U-Net
performance. ResNet (Cicek et al., 2016) is also a common backbone for the
U-Net architecture which addresses the issues of stacking many layers in deep
neural networks that causes vanishing gradient problem. The Google inception
module, widely utilized for extracting features across multiple scales, was
initially introduced in InceptionV1, where kernels of different sizes were
concatenated in parallel (Szegedy et al., 2015, 2016, 2017; Zhang et al.,
2021). This architecture underwent further refinements in subsequent versions,
with InceptionV2 replacing the 5$\times$5 convolution with two stacked
3$\times$3 convolutions and InceptionV4 breaking down square convolutional
kernels into two vectors to reduce computational operations while increasing
the receptive field (Szegedy et al., 2016, 2017; Al Shoura et al., 2023).
However, this led to a limitation where larger kernels could not be broken
down, resulting in fewer selectable features. MultiRes blocks, which employ a
series of convolutional layers with residual connections, have been utilized
to provide features at different scales, although with limited efficacy for
small images and fuzzy objects (Ibtehaz and Rahman, 2020; Hossain et al.,
2023; Lou et al., 2021). To overcome these limitations, the dual-channel UNet
(DC-UNet) was proposed to incorporate more different-scale features at the
cost of increased network parameters and GPU memory consumption (Lou et al.,
2021; Ansari et al., 2022). Gridach (2021); Jiang et al. (2019); Lou et al.
(2022); Yang et al. (2020); Fu et al. (2023); Wang et al. (2021b); Zhan et al.
(2023) consider fixed numbers of features for each multi-scale dilated atrous
receptive field in parallel to not increase the computational complexity,
which is another way of representing information at various scales. This
strategy increases the receptive field of the layer without adding additional
network parameters. A convolutional filter in a CNN can be decomposed as a
linear combination of pre-fixed bases particularly Fourier-Bessel bases (Qiu
et al., 2018). Wang et al. (2021a) combine the idea of adaptive convolutional
kernel and combination of pre-fixed bases and replace all convolution filters
with adaptive atoms which are shown slightly better in Image Classification
tasks particularly when dealing with intra-image variance, while it is not yet
applied for image segmentation tasks.

Figure 2: Adaptive Convolution Layer added to the leading UCTransNet
architecture. The coefficient generator network generates the weights for
Fourier-Bessel bases with different sizes for each pixel and channel. It
results a fixed kernel to be convolved for that pixel.

In summary, recently many approaches are proposed to improve segmentation
performance that are mainly concentrated on the improving skip connections,
including attention mechanism and transformer and improving the backbone such
as including dynamic convolution. The UCTransNet (Wang et al., 2022) performs
better compare to other approaches such as TransUNet (Chen et al., 2021),
Residual U-Net (Zhang et al., 2018), MultiResUNet (Ibtehaz and Rahman, 2020),
U-Net++ (Zhou et al., 2018), Att-UNet (Oktay et al., 2018) and original U-Net
(Ronneberger et al., 2015) over several datasets such as ISIC 2018, and SegPC
2021 (Azad et al., 2022).

Although employing a dynamic receptive field offers theoretical advantages, it
is still challenging to implement in practice. On one side, certain approaches
necessitate extensive computational resources and memory to calculate the
dynamic receptive field; on the other, they may not yield any enhancement in
performance. After a comprehensive study of various adaptive methods, the rest
of this paper will present a novel dynamic receptive field layer that can be
placed ahead of leading models. It improves the recognition performance while
maintaining a similar number of parameters.

##  3 Proposed Approach

In this section, we present an adaptive convolution layer to be incorporated
into leading models. Though it is not exclusive to UCTransNet, we showcase its
integration at the top of this architecture, naming it AdaptUCTransNet. The
AdaptUCTransNet dynamically adjusts the kernel size based on the local context
of the input image, enabling the network to capture relevant features at
multiple scales from the first layer, leading to improved segmentation
accuracy, robustness, and better consider diverse anatomical structures,
irregular shapes, and varying feature scales in medical images. Additionally,
leveraging the benefits of the UCTransNet architecture, which integrates U-Net
and transformer models, AdaptUCTransNet combines spatial information with
self-attention mechanisms to extract meaningful features from medical images.

In MIS, the presence of diverse segments necessitates the adoption of a
flexible approach. By adjusting the kernel size based on the specific context
of each pixel, we can have a higher level of discernment when extracting
features within the inner layers of deep networks. By liberating the network
from strict reliance on the hyperparameter associated with the dataset for
determining the kernel size, we empower it to adapt and optimize its
performance according to the unique characteristics of the input data.
Therefore, the proposed approach is to add the adaptive multi-size kernel
convolution layer to the best deep learning model for MIS. We leverage the
fact that convolution layers can be mathematically expressed as a linear
combination of predetermined bases (Qiu et al., 2018). By employing a limited
number of Fourier-Bessel bases, we substantially reduce the number of
parameters. Notably, this reduction in parameters does not compromise the
accuracy of image classification tasks (Qiu et al., 2018; Wang et al., 2021c),
while it has not yet been explored for MIS. Additionally, using Fourier-Bessel
bases improve the recognition of the structural information of the input image
and effectively mitigates the impact of high-frequency noise and addresses the
computational complexities associated with employing multiple kernel sizes
within the convolution layer.

Formally, the symbol $[p]$ is employed to denote the spatial coordinates on a
feature map $Z$. These coordinates are applicable to a range of dimensional
structures, extending from one-dimension to more complex, higher-dimensional
forms. The notation $\mathcal{N}^{\delta}_{Z[p]}$ is used to represent the
receptive field surrounding the feature vector $Z[p]$, where the distance is
within a $\delta$. The size of this receptive field can vary, ranging from
being quite small to encompassing the entirety of the input feature.
Considering this receptive field, the $\mathcal{T}$ is used to represent
transformed inner receptive field. For instance, in the dilated receptive
field, $\mathcal{T}$ selects a subset of input features at specified
intervals. Conversely, in the case of a traditional convolution layer,
$\mathcal{T}$ encompasses all pixels, incorporating them without any
alterations. Then, traditionally, this inner receptive field is convolved with
a set of kernels, denoted as $K$. The convolution operation can be
mathematically expressed as
$Z^{\prime}[p]=K*\mathcal{T}(\mathcal{N}^{\delta}_{Z[p]})$, where $Z^{\prime}$
denotes the resultant feature map post-convolution, and $*$ operation sums the
element-wise product. i.e., this can be expressed as
$K*N=\sum_{q}{(K\>\circ\>N)}_{q}$, where $\>\circ\>$ signifies Hadamard
element-wise multiplication and the sum is taken over all elements of the
product. These kernels are learned end-to-end by the network during the
training process, typically via backpropagation and gradient descent. The
inclusion of these kernels results in the addition of extra parameters to the
network. As expected, using larger kernels further increases the total
parameter count. Therefore, it will be more challenging to dynamically select
the most suitable kernel size for each spatial coordinate.

Based on Qiu et al. (2018), a convolution kernel can be decomposed as a
combination of Fourier-Bessel bases. Therefore, instead of using a learnable
kernel, we can learn the weights ($W$) for the pre-fixed Fourier-Bessel bases
with different sizes ($FS$). Therefore, $Z^{\prime}[p]=FS\times
W*\mathcal{T}(\mathcal{N}^{\delta}_{Z[p]})$. For adaptively changing the
receptive field, we use another inner network to learn these weights ($W$)
based on the receptive field $\mathcal{N}^{\delta}_{Z[p]}$. This inner
network, called the coefficient generator network, is trained end-to-end with
the backpropagation and gradient descent. In the process of selecting
appropriate weights for kernels of varying sizes, we stack several layers of
smaller kernels to control the complexity, as suggested by Simonyan and
Zisserman (2015). This approach ensures that the output comprehensively covers
the entire receptive field. For example, by stacking a minimum of four layers
of $3\times 3$ kernels, we can achieve $9\times 9$ receptive field. This multi
layer network convolved through the receptive field
$\mathcal{N}^{\delta}_{Z[p]}$ with a smaller kernel size, and the output of
this network is the local weights ($W({\mathcal{N}^{\delta}_{Z[p]}})$) for the
pre-fixed Fourier-Bessel bases with different sizes ($FS$). This inner network
remains fixed across all receptive fields. Therefore, this approach maintains
the translation invariance characteristic of convolutional networks while
taking into account the local context.

Building upon the specific attributes of medical images, we present our
framework to enhance the accuracy of the best segmentation deep network. The
inclusion of our adaptive layer does lead to an increase in the number of
parameters, but this increase constitutes only a minor fraction of the entire
parameters of the network (section 4.4). A graphical representation of the
network architecture is provided in figure 2. Given set $F$ of Fourier-Bessel
bases with $\left|S\right|$ different sizes denotaed as $FS$, for every pixel
and channel another receptive field (local neighbors of the corresponding
pixel which is greater than $S$) will be convolved in the coefficient
generator network using smaller kernels. This network will be trained end to
end in the training phase of the whole network. It produces $W$ with size
$\left|F\right|\times\left|S\right|\times m$ weights, where $m$ is the number
of intermediate channels. Matrix multiplication of Fourier-Bessel bases ($FS$)
and these weights ($W$), generates a kernel for the given pixel and channel.
The adaptive layer convolves the input image with the explained kernel,
resulting in $m$ intermediate features for each pixel and channel. Then, these
intermediate features will be fed to the leading segmentation technique
UCTransNet (Wang et al., 2022), which replaces the simple skip connections in
U-Net with a multi-scale channel-wise module. By using this combination, we
can improve the performance of the model.

##  4 Experiments

In the experiments, we conduct an extensive evaluation of the adaptive layer
added ahead of UCTransNet, AttUNet and well-known U-Net architecture using
benchmark medical image datasets. By comparing the results with traditional
CNNs that employ fixed kernel sizes, we demonstrate the superior performance
and generalizability of our adaptive approach. Experiments are conducted on
various public testbeds, including the Multiple Myeloma Plasma Cell
Segmentation (SegPC) 2021 (Gupta et al., 2023, 2021) and the International
Skin Imaging Collaboration (ISIC) 2018 datasets (Codella et al., 2019), which
will be explained in details in the next sub section. Then, after explaining
the details of implementation, we present the experimental results.

###  4.1 Environment Setup

In order to foster transparency and repeatability of our work, All the codes,
datasets, and documentation are freely accessible on our GitHub repository:
https://github.com/modaresimr/adaptive_mis. All experiments are run on an
NVIDIA DGX-1 machine featuring a Tesla V10032 GPU, Intel Xeon E5-2698v4 CPUs,
and 512 GB of RAM. However, we use only a part of these resources.

###  4.2 Datasets

Experiments are conducted on various public testbeds, including the Multiple
Myeloma Plasma Cell Segmentation (SegPC) 2021 (Gupta et al., 2023, 2021) and
the International Skin Imaging Collaboration (ISIC) 2018 datasets (Codella et
al., 2019). SegPC contains a collection of 775 microscopic 2D images from the
bone marrow samples of Multiple Myeloma patients. It significantly helped
hematologists in making more accurate diagnoses and facilitates cancer
screening. The dataset from ISIC 2018 boasts a large collection of 2,594 RGB
dermoscopy images. Robust segmentation of these images plays a crucial role in
medical diagnosis and is challenging due to inconsistent lighting conditions,
varying lesion sizes, texture disparities, and differences in color and
positioning. Moreover, the presence of unrelated elements like air bubbles,
hair strands, or ruler markers further add to the complexity (Hasan et al.,
2020; Codella et al., 2019; Gupta et al., 2023, 2021; Azad et al., 2022). Both
datasets are illustrated in figures 3 and 4 with the segmentation result of
our approach.

Similar to the work by Azad et al. (2022), we allocated 70% of images for
training, 10% for validation, and the remaining 20% for testing, and our
research focused on the segmentation of Cytoplasm components in SegPC 2021 and
segmentation of cancer lesions in ISIS 2018 datasets.

###  4.3 Hyperparameters and Implementation Details

Our pipeline infers the segments from raw image data. All images underwent a
sizing down operation to a standard size of $224\times 224$ pixels. The
pipeline is composed of an adaptive convolution layer with the kernel size of
3, 5, 7, and 9. We also select six Fourier Bessel bases similar to Wang et al.
(2021a). For the coefficient generator network, we have used six intermediate
features ($m$), which, is responsible for encoding the weights of the prefixed
bases. This network will be trained end to end during the global training
process. We maintain early stopping with a patience of 20 epochs during the
training. For better comparison, we make the other hyperparameters similar to
the ones used in (Azad et al., 2022), such as the batch size of 16, epochs
limit of 100, Adam optimizer with a learning rate of 0.0001, and the average
of cross-entropy loss and dice loss for the loss function. The entire
implementation, along with hyperparameters, is accessible and verifiable
through our publicly available open-source repository.

###  4.4 Model Complexity

An essential factor in the assessment of models is the computational
complexity. The number of trainable parameters of these components is listed
in table 1. Therefore, although the training complexity is similar (the
differences are less than 2%), its performance is better regarding to tables 2
and 3.

Table 1: Number of million parameters in each model, including the adaptive variant. This indicates that although our method is effective in determining the ideal dynamic kernel size, it keeps the number of parameters almost the same as those of the original model. Methods | Normal | with Adaptive Layer
---|---|---
U-Net | 19.487 | 19.850
Att-UNet | 34.879 | 35.242
UCTransNet | 66.431 | 66.794

###  4.5 Evaluation Metrics

We used a series of performance metrics for a comprehensive analysis of our
model’s effectiveness. Precision and Recall served as the primary metrics.
Precision assesses the model’s ability to correctly predict positive
instances, while Recall measures the completeness of these positive
predictions. Although Accuracy gives a general idea of the model’s overall
performance, it’s crucial to interpret it alongside the other metrics due to
potential data imbalance. We utilized the Intersection over Union (IoU) to
measure the overlap between the predicted segmentation and the actual one.

As for the Dice Coefficient, it was used as an alternative to the F1 score due
to its increased relevance in medical imaging. It places double emphasis on
true positives which is the harmonic mean of precision and recall. It
effectively gauges the spatial overlap of the predictions, which is
particularly useful in biomedical image segmentation tasks.

###  4.6 Experimental Results

Figure 3: Visual comparisons of different methods for cytoplasm segmentation
(depicted as the white region) on the SegPC 2021 dataset. The blue region
denotes the Nucleus area of a cell. The initial column displays the input
image, while the second column presents the ground truth. Following these, the
subsequent columns feature the models along with their adaptive versions. As
is evident, models incorporating the adaptive layer more accurately recognize
the shape of the cytoplasm, and this improvement is particularly greater in
larger segments.  Figure 4: Segmentation output of various deep model in ISIC
2018 dataset. The white region represents the ground truth that remains
undetected (FN), while the gray region represents the detected ground truth
(TP), and red denotes the FP. The columns orders are similar to figure 3. Once
again, our model is more effective in identifying target regions, particularly
noticeable in larger ones where traditional models with fixed kernels face
difficulties in detecting intra-size features.

Figure 5: The train loss and validation loss of SegPC 2021 dataset (left) and
ISIC 2018 dataset (right). They indicate that the models are neither
overfitting nor underfitting.

####  4.6.1 Cell (SegPC 2021)

We have showcased the visual segmentation results from the SegPC 2021 dataset
in figure 3. Our adaptive multi-size-kernel representation effectively
demonstrates its aptitude to generate more accurate segmentation for cells of
diverse scales and backgrounds. A detailed comparison of results for the SegPC
2021 dataset is provided in table 2, further highlighting the effectiveness of
our methodology. They demonstrate that incorporating our adaptive layer into
the first layer of the model enhances the extraction of structural
information, leading to a better virtual representation. In
LABEL:fig:segpc2021-train__loss_validate__loss, we have plotted the training
and validation loss curves for the SegPC 2021 dataset. These curves indicate
the robust performance of the model, as it is neither underfitting nor
overfitting.

Table 2: Comparison of results for the SegPC 2021 dataset. Each experiment is repeated five times. We have added our adaptive layer to two leading models (AttUNet, UCTransnet), and traditional UNet. As it is shown, even for architectures that internal include multi scale module, such as UCTransnet, adding our adaptive layer on top of that netwok improves not only the performance of these models but also their consistency (as shown by the standard deviation). For a more comprehensive comparison, other deep models, such as Missformer, ResUNet, and MultiResUNet, are included at the second part of the table. Their models with our adaptive layer are accessible in our repository. Model | Accuracy | Dice | IoU
---|---|---|---
adapt_UCTransnet | 98.66±0.01 | 92.11±0.02 | 91.96±0.02
UCTransnet | 98.61±0.04 | 91.85±0.23 | 91.71±0.22
adapt_AttUNet | 98.71±0.01 | 92.41±0.03 | 92.25±0.03
AttUNet | 98.65±0.02 | 92.10±0.08 | 91.95±0.08
adapt_UNet | 98.22±0.01 | 89.58±0.13 | 89.60±0.11
UNet | 98.07±0.05 | 88.69±0.35 | 88.80±0.30
Missformer | 98.35±0.04 | 90.38±0.16 | 90.32±0.15
ResUNet | 97.74±0.04 | 86.70±0.15 | 87.04±0.14
MultiResUNet | 96.15±0.41 | 80.29±1.70 | 81.46±1.41

####  4.6.2 Skin Cancer (ISIC 2018) dataset

The segmentation result of our approach is illustrated in figure 4. Skin
lesions typically manifest within the texture and seldom adhere to a definite
shape or geometric pattern. This unpredictable behavior might explain why
transformer-based networks may not yield substantial benefits for texture-
related patterns (Azad et al., 2022). Yet again, the adaptive multi-size-
kernel representation capability of our methodology demonstrates its
proficiency. Compared to other approaches, it is remarkably effective at
localizing abnormal regions, which is clearly illustrated in the segmentation
results shown in figure 4. This calls for a deeper exploration of the
robustness and applicability of our approach. In
LABEL:fig:isic2018-train__loss_validate__loss_VS_epoch.jpeg, we have plotted
the training and validation loss curves for the ISIC 2018 dataset. The model
is performing well, as indicated by the minimal gap (less than 0.003) between
the training and validation losses and their stable behavior.

###  4.7 Discussion

The conducted experiments substantiate the efficacy of integrating an adaptive
layer at the initial stage of deep networks, enhancing their resilience to
diverse scales. This layer augments the network’s capability to discern
structural information across varying sizes, while maintaining a comparable
parameter count. The experiments demonstrate that the inputs including larger
segments are better recognized by the proposed method. The noteworthy aspect
of these experiments was the enhancement of all existing models through the
integration of the adaptive layer, without necessitating any modifications to
their structure. This improvement was observed even in models that inherently
feature a multi-scale module (such as UCTransnet). The accuracy of our
experiment and number of parameters aligns with the recent survey by Azad et
al. (2022), further validating the credibility of our experimental results.

Table 3: Comparison of results for the ISIC 2018 dataset. It shows the effectiveness of our methodology. Similar to table 2, Each experiment is repeated five times and we have added our adaptive layer to two leading models (AttUNet, UCTransnet), and traditional UNet. This approach improves the performance of even the models with internal multi-scale module. For a more comprehensive comparison, other deep models, such as Missformer, ResUNet, and MultiResUNet, are included at the second part of the table. Their models with our adaptive layer are accessible in our repository. Model | Accuracy | Dice | IoU
---|---|---|---
adapt_UCTransnet | 95.64±0.13 | 89.31±0.18 | 87.68±0.16
UCTransnet | 95.54±0.07 | 89.04±0.27 | 87.40±0.25
adapt_AttUNet | 95.57±0.16 | 88.96±0.28 | 87.36±0.32
AttUNet | 95.44±0.15 | 88.66±0.25 | 87.04±0.29
adapt_UNet | 94.80±0.21 | 87.09±0.29 | 85.41±0.36
UNet | 94.43±0.25 | 86.18±0.39 | 84.49±0.46
Missformer | 95.25±0.18 | 88.38±0.42 | 86.69±0.43
ResUNet | 94.35±0.09 | 85.84±0.07 | 84.19±0.09
MultiResUNet | 92.83±0.63 | 84.01±1.02 | 81.80±1.15

##  5 Conclusion

In this research, we delved into recent advancements in the field of computer
vision, focusing on the dynamic modification of the receptive field, a
strategy bearing resemblance to the previously described windowing approach.
We introduced a novel adaptive layer designed for integration into MIS, aiming
to enhance their overall performance. We have shown the effectiveness of our
adaptive layer approach by including a dynamic layer on the top of the best
segmentation deep network. This approach improves the recognition performance
by dynamically changing the receptive field, resulting better identification
of structural information and various size targets, and reducing high
frequency noises while keeping the number of parameters nearly unchanged.
These promising results highlight the potential of our approach for broader
applications in medical imaging. Future research could explore adding the
adaptive layer to the end of the network, potentially improving output
processing, with initial signs suggesting further gains. We also posit that
the integration of an adaptive layer ahead of a lower-parameter inner network
might achieve, or even surpass, the performance achieved with that higher-
parameter inner network, that needs deeper investigation in future studies.

## Funding Declaration

This research did not receive any specific grant from public, commercial, or
not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no conflict of interest.

## Data Availability Statement

The datasets, codes, and framework used in this study are accessible online
through our repository at https://github.com/modaresimr/adaptive_mis.

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