Skin Image Analysis in the Age of Deep Learning Call for Papers

IEEE Journal of Biomedical and Health Informatics (IEEE JBHI) 
special issue on 
Skin Image Analysis in the Age of Deep Learning

IEEE JBHI is seeking original and unpublished manuscripts for a
special issue on Skin Image Analysis in the Age of Deep Learning.

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.

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.

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.

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

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.

Only high-quality and original research contributions will be
considered. The special issue will highlight, but not be limited to,
the following topics:

+ Computer Vision in Dermatology and Primary Care
+ Few-Shot Learning for Dermatological Conditions
+ Skin Analysis from Dermoscopic Images
+ Skin Analysis from Clinical Photographs
+ Skin Analysis from Total-Body Photography and 3D Skin Reconstructions
+ Skin Analysis from Confocal Microscopy
+ Skin Analysis from Optical Coherence Tomography (OCT)
+ Skin Analysis from Histopathological Images
+ Skin Analysis from Multi-Modal Data Sources
+ Explainable Artificial Intelligence (XAI) Related to Skin Image Analysis
+ Algorithms to Mitigate Class Imbalance
+ Uncertainty Estimation Related to Skin Image Analysis
+ Application Workflows for Skin Image Analysis
+ Robustness to Bias from Clinical and User-Originating Photography

Note that while the issue focuses on deep learning-based approaches,
outstanding contributions from other subfields of machine learning
will also be considered.

Guest Editors
+ M. Emre Celebi, University of Central Arkansas, Conway, AR, USA,

+ Catarina Barata, Instituto Superior Técnico, Lisbon, Portugal,

+ Allan Halpern, Memorial Sloan Kettering Cancer Center, New York
City, NY, USA,

+ Philipp Tschandl, Medical University of Vienna, Vienna, Austria,

+ Marc Combalia, Hospital Clínic de Barcelona, Barcelona, Spain,

+ Yuan Liu, Google Health, Palo Alto, CA, USA,

Key Dates
Submission deadline: September 1, 2021
First reviews due: October 15, 2021
Revised manuscripts due: December 1, 2021
Final decisions: January 15, 2022