3 research outputs found
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling
automated clinical decision procedures. However, prevailing supervised deep
learning approaches for medical image segmentation encounter significant
challenges due to their heavy dependence on extensive labeled training data. To
tackle this issue, we propose a novel self-supervised algorithm,
\textbf{S-Net}, which integrates a robust framework based on the proposed
Inception Large Kernel Attention (I-LKA) modules. This architectural
enhancement makes it possible to comprehensively capture contextual information
while preserving local intricacies, thereby enabling precise semantic
segmentation. Furthermore, considering that lesions in medical images often
exhibit deformations, we leverage deformable convolution as an integral
component to effectively capture and delineate lesion deformations for superior
object boundary definition. Additionally, our self-supervised strategy
emphasizes the acquisition of invariance to affine transformations, which is
commonly encountered in medical scenarios. This emphasis on robustness with
respect to geometric distortions significantly enhances the model's ability to
accurately model and handle such distortions. To enforce spatial consistency
and promote the grouping of spatially connected image pixels with similar
feature representations, we introduce a spatial consistency loss term. This
aids the network in effectively capturing the relationships among neighboring
pixels and enhancing the overall segmentation quality. The S-Net approach
iteratively learns pixel-level feature representations for image content
clustering in an end-to-end manner. Our experimental results on skin lesion and
lung organ segmentation tasks show the superior performance of our method
compared to the SOTA approaches. https://github.com/mindflow-institue/SSCTComment: Accepted in ICCV 2023 workshop CVAM
Medical Image Segmentation Review: The success of U-Net
Automatic medical image segmentation is a crucial topic in the medical domain
and successively a critical counterpart in the computer-aided diagnosis
paradigm. U-Net is the most widespread image segmentation architecture due to
its flexibility, optimized modular design, and success in all medical image
modalities. Over the years, the U-Net model achieved tremendous attention from
academic and industrial researchers. Several extensions of this network have
been proposed to address the scale and complexity created by medical tasks.
Addressing the deficiency of the naive U-Net model is the foremost step for
vendors to utilize the proper U-Net variant model for their business. Having a
compendium of different variants in one place makes it easier for builders to
identify the relevant research. Also, for ML researchers it will help them
understand the challenges of the biological tasks that challenge the model. To
address this, we discuss the practical aspects of the U-Net model and suggest a
taxonomy to categorize each network variant. Moreover, to measure the
performance of these strategies in a clinical application, we propose fair
evaluations of some unique and famous designs on well-known datasets. We
provide a comprehensive implementation library with trained models for future
research. In addition, for ease of future studies, we created an online list of
U-Net papers with their possible official implementation. All information is
gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine
Intelligence Journa
Applications of Neural Networks in Biomedical Data Analysis
Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields