ISIC 2022 Workshop Banner

[ Introduction | Invited Speakers | Important Dates | Paper Submission | Datasets | Program Schedule | Organizers ]

05/03/2022: Datasets updated
05/02/2022: Important dates updated
04/23/2022: Workshop format updated
04/05/2022: Venue and important dates updated
02/22/2022: Website launched

Seventh ISIC Skin Image Analysis Workshop
@ ECCV 2022

Hosted by the International Skin Imaging Collaboration (ISIC)


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. In addition, dermatological complaints are also among the most prevalent in primary care (Lowell et al., 2001). Images of the skin are the most easily captured form of medical image in healthcare, and the domain shares qualities to standard computer vision datasets, serving as a natural bridge between standard computer vision tasks and medical applications. 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 domains, varying genetics, disease states, imaging equipment, and imaging conditions can significantly change the appearance of the skin, making localization and classification in this domain unsolved tasks.

This workshop will serve as a venue to facilitate advancements and knowledge dissemination in the field of skin image analysis, raising awareness and interest for these socially valuable tasks. Invited speakers include major influencers in computer vision and skin imaging, and authors of accepted papers.

Lowell et al. “Dermatology in Primary Care: Prevalence and Patient Disposition,” Journal of the American Academy of Dermatology, vol. 45, no. 2, pp. 250–255, 2001.

Topics of interest include:

  • 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 Video
  • 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 ex-vivo and Fluorescence Microscopy
  • 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
  • Human-Computer Interaction & Application Workflows for Skin Image Analysis
  • Robustness to Bias from Clinical and User-Originating Photography
  • Assessing and Creating Fairness of Skin Analysis in Underrepresented Groups
The workshop will give out 2 awards towards paper submissions:
  • Best Paper Award: $4,000 USD 
  • Honorable Mention Award: $2,000 USD
Judging will be carried out by the workshop chairs based on the reviewer comments, novelty, potential impact, and manuscript quality.

Invited Speakers

The workshop will feature several prominent names in the field of skin image analysis, including:

  • Yuan Liu (Senior Researcher, Google Health, Palo Alto, CA, USA) [confirmed]
  • Eduardo Valle (Assistant Professor, University of Campinas, Campinas, SP, Brazil) [confirmed]
  • Veronica Rotemberg (Dermatologist, Memorial Sloan Kettering Cancer Center, New York City, NY, USA) [confirmed]

Important Dates

July 17, 2022: Workshop Paper Submission Deadline (11:59 am Pacific Time)
August 7, 2022: Author Notifications
August 21, 2022: Camera-Ready Submission Deadline (11:59 am Pacific Time)
October 23 or 24, 2022: Virtual Workshop @ ECCV 2022 (Exact Date & Time: TBA)

Paper Submission

For paper submissions, ECCV guidelines are followed. Accepted papers will be published in the ECCV Workshop Proceedings and archived in the SpringerLink digital library.

Manuscript Submission System

Public Datasets for Skin Image Analysis Research

  • Derm7pt: Over 2,000 dermoscopic and clinical images of skin lesions with 7-point checklist criteria  and diagnostic category information.
  • Dermofit Image Library: 1,300 clinical images of skin lesions with diagnostic category information and segmentation masks.
  • Diverse Dermatology Images: 656 clinical images of skin lesions with diverse skin tone  representation and diagnostic category information.
  • Fitzpatrick 17k: 16,577 clinical images with skin condition labels and skin type labels based on the Fitzpatrick scoring system.
  • ISIC 2018ISIC 2019ISIC 2020: The ISIC has organized the world’s largest repository of dermoscopic images of skin (157,000+ images, 69,000+ of which are publicly available) to support research and development of methods for segmentation, feature extraction, and classification. These datasets are snapshots used for the 2018, 2019, and 2020 ISIC melanoma detection challenges. See also the HAM10000 and BCN20000 datasets.
  • MED-NODE: 170 clinical images of skin lesions with diagnostic category information.
  • PAD-UFES-20: Over 2,200 clinical images of skin lesions with associated metadata.
  • PH2: 200 dermoscopic images of melanocytic lesions with detailed annotation. 
  • SD-128 / SD-198 / SD-260: 6,584 clinical photographs covering 128/198/260 distinct skin disorders with associated metadata.



Workshop Organizers:

Steering Committee:

Tentative Program Committee:

  • Euijoon Ahn, University of Sydney, Australia
  • Sandra Avila, University of Campinas, Brazil
  • Rafael Garcia, University of Girona, Spain
  • ZongYuan Ge, Monash University, Australia
  • Iván González‐Díaz, University Carlos III of Madrid, Spain
  • Manu Goyal, UT Southwestern Medical Center, USA
  • Ghassan Hamarneh, Simon Fraser University, Canada
  • Joanna Jaworek‐Korjakowska, AGH University of Science and Technology, Poland
  • Reda Kasmi, University of Bouira, Algeria
  • Parneet Kaur, Johnson & Johnson, USA
  • Jeremy Kawahara, Simon Fraser University, USA
  • Jinman Kim, University of Sydney, Australia
  • Sinan Kockara, University of Central Arkansas, USA
  • Kivanc Kose, Memorial Sloan Kettering Cancer Center, USA
  • Tim K. Lee, University of British Columbia, Canada
  • Ali Madooei, Johns Hopkins University, USA
  • Amirreza Mahbod, Medical University of Vienna, Austria
  • Roberta Oliveira, University of Brasilia, Brazil
  • André G. C. Pacheco, Samsung R&D Institute, Brazil
  • Linlin Shen, Shenzhen University, China
  • Eduardo Valle, University of Campinas, Brazil

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