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<div>IEEE Journal of Biomedical and Health Informatics (IEEE JBHI) special issue on Skin Image Analysis in the Age of Deep Learning<br><br><a href="https://www.embs.org/jbhi/special-issues-page/special-issue-on-skin-image-analysis-in-the-age-of-deep-learning/" target="_blank">https://www.embs.org/jbhi/special-issues-page/special-issue-on-skin-image-analysis-in-the-age-of-deep-learning/</a><br><br>IEEE
JBHI is seeking original and unpublished manuscripts for a special
issue on Skin Image Analysis in the Age of Deep Learning.</div><div><br></div><div>Skin
is the largest organ of the human body, and is the first area of a
patient assessed by clinical staff. The skin delivers numerous insights
into a patient’s underlying health: for example, pale or blue skin
suggests respiratory issues, unusually yellowish skin can signal hepatic
issues, or certain rashes can be indicative of autoimmune issues.</div><br>Dermatological
complaints are the most prevalent reason that patients seek primary
care, and images of the skin are the most easily captured form of
medical image in healthcare. However, certain serious skin diseases are
not reliably diagnosed by primary care. For example, while unaided
visual inspection by expert dermatologists yields about 60% accuracy for
detecting melanoma, the most dangerous type of skin cancer, primary
care clinicians achieve only 23–46% accuracy. Therefore, there is a
clear a need to scale expertise for robust skin disease classification.<br><br>Out
of all medical imaging datasets, skin images are the most similar to
other standard computer vision datasets. However, significant and unique
challenges still exist in this domain. For example, there is remarkable
visual similarity across disease conditions, and compared to other
medical imaging modalities, varying genetics, disease states, imaging
equipment, and imaging conditions can significantly change the
appearance of skin, making localization and classification in this
domain unsolved tasks.<br><br>In recent years, several datasets have
become publicly available to support research and development in
automated skin image analysis across various imaging modalities,
including dermoscopy and clinical photographs. These developments have
spiked an interest in research around skin image analysis. According to
Google Scholar, at the time of this writing, there are over 1,600
research works that use or cite the ISIC Skin Cancer publications,
resources, and benchmark challenges.<br><br>With the release of large
public datasets, development of novel learning algorithms and network
architectures with open-source implementations, and the availability of
powerful and inexpensive graphics processing units, deep learning has
become the technique of choice in a wide variety of medical image
analysis problems over the past decade. Skin image analysis is no
exception, as demonstrated by the large number of deep learning-based
contributions/entries submitted to our past five ISIC
Workshops/Challenges. The goals of this special issue are to facilitate
advancements and knowledge dissemination in deep learning-based skin
image analysis, raising awareness and interest for these socially
valuable tasks. The intended audience includes researchers and
practicing clinicians, who are increasingly using digital analytic
tools.<br><br>Only high-quality and original research contributions will
be considered. The special issue will highlight, but not be limited to,
the following topics:<br><br>+ Computer Vision in Dermatology and Primary Care<br>+ Few-Shot Learning for Dermatological Conditions<br>+ Skin Analysis from Dermoscopic Images<br>+ Skin Analysis from Clinical Photographs<br>+ Skin Analysis from Total-Body Photography and 3D Skin Reconstructions<br>+ Skin Analysis from Confocal Microscopy<br>+ Skin Analysis from Optical Coherence Tomography (OCT)<br>+ Skin Analysis from Histopathological Images<br>+ Skin Analysis from Multi-Modal Data Sources<br>+ Explainable Artificial Intelligence (XAI) Related to Skin Image Analysis<br>+ Algorithms to Mitigate Class Imbalance<br>+ Uncertainty Estimation Related to Skin Image Analysis<br>+ Application Workflows for Skin Image Analysis<br>+ Robustness to Bias from Clinical and User-Originating Photography<br><br>Note
that while the issue focuses on deep learning-based approaches,
outstanding contributions from other subfields of machine learning will
also be considered.<br><br>Guest Editors<br>------------------<br>+ M. Emre Celebi, University of Central Arkansas, Conway, AR, USA, <a href="mailto:ecelebi@uca.edu" target="_blank">ecelebi@uca.edu</a><br>+ Catarina Barata, Instituto Superior Técnico, Lisbon, Portugal, <a href="mailto:ana.c.fidalgo.barata@ist.utl.pt" target="_blank">ana.c.fidalgo.barata@ist.utl.pt</a><br>+ Allan Halpern, Memorial Sloan Kettering Cancer Center, New York City, NY, USA, <a href="mailto:halperna@mskcc.org" target="_blank">halperna@mskcc.org</a><br>+ Philipp Tschandl, Medical University of Vienna, Vienna, Austria, <a href="mailto:philipp.tschandl@meduniwien.ac.at" target="_blank">philipp.tschandl@meduniwien.ac.at</a><br>+ Marc Combalia, Hospital Clínic de Barcelona, Barcelona, Spain, <a href="mailto:mcombalia@clinic.cat" target="_blank">mcombalia@clinic.cat</a><br>+ Yuan Liu, Google Health, Palo Alto, CA, USA, <a href="mailto:yuanliu@google.com" target="_blank">yuanliu@google.com</a><br><br>Key Dates<br>--------------<br>Submission deadline: September 1, 2021<br>First reviews due: October 15, 2021<br>Revised manuscripts due: December 1, 2021<br>Final decisions: January 15, 2022
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