[visionlist] Call for Papers: IEEE JBHI special issue on Skin Image Analysis in the Age of Deep Learning
ecelebi at uca.edu
Mon Jun 7 16:44:11 -04 2021
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IEEE JBHI is seeking original and unpublished manuscripts for a special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 challenges.
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
+ 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.
+ M. Emre Celebi, University of Central Arkansas, Conway, AR, USA,
ecelebi at uca.edu
+ Catarina Barata, Instituto Superior Técnico, Lisbon, Portugal,
ana.c.fidalgo.barata at ist.utl.pt
+ Allan Halpern, Memorial Sloan Kettering Cancer Center, New York City, NY,
USA, halperna at mskcc.org
+ Philipp Tschandl, Medical University of Vienna, Vienna, Austria,
philipp.tschandl at meduniwien.ac.at
+ Marc Combalia, Hospital Clínic de Barcelona, Barcelona, Spain,
mcombalia at clinic.cat
+ Yuan Liu, Google Health, Palo Alto, CA, USA, yuanliu at google.com
Submission deadline: September 1, 2021
First reviews due: October 15, 2021
Revised manuscripts due: December 1, 2021
Final decisions: January 15, 2022
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