63 research outputs found
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
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