Artificial Intelligence Working Group

Goal

The AI working group's goal is to promote the development of accurate and effective computerized systems to aid melanoma diagnosis and clinical decision support by creating resources for and engaging the dermatology and computer science communities.

We engage our stakeholder communities through meetings, publications and conferences. We also host AI grand and live challenges.

Accomplishments

Publications

Based on our AI challenges, over 1,000 scientific papers were written by ISIC members and other inspired individuals from various communities. Publications our members were involved in include:

Codella, Noel CF, et al. "Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)." 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018.

Gutman, David, et al. "Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC)." arXiv preprint arXiv:1605.01397 (2016).

Codella, Noel, et al. "Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic)." arXiv preprint arXiv:1902.03368 (2019).

Veronica, Rotemberg, et al. "A patient-centric dataset of images and metadata for identifying melanomas using clinical context." Scientific Data 8.1 (2021).

Marchetti, Michael A., et al. "Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images." Journal of the American Academy of Dermatology 78.2 (2018): 270-277.

Tschandl, Philipp, et al. "Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study." The Lancet Oncology 20.7 (2019): 938-947.

Tschandl, Philipp, et al. "Human–computer collaboration for skin cancer recognition." Nature Medicine 26.8 (2020): 1229-1234.

Rotemberg, Veronica, et al. "The role of public challenges and data sets towards algorithm development, trust, and use in clinical practice." Seminars in cutaneous medicine and surgery. Vol. 38. No. 1. 2019.

Workshops and Conferences

The ISIC workshops and conferences have achieved significant participation and recognition and have included the most prestigious and prominent computer science and dermatology conferences across the globe.

AI Challenges

To date we have hosted 5 ISIC challenges, which engaged thousands of participants from the computer science communities.

ISIC Archive

Our primary focus is the development of the ISIC Archive, a large and expanding open source public access archive of annotated skin images. The Archive currently contains over 100,000 images.

Goal

We engage our stakeholder communities through meetings, publications and conferences. We also host AI grand and live challenges.

Publications

Based on our AI challenges, over 1,000 scientific papers were written by ISIC members and other inspired individuals from various communities. Publications our members were involved in include:

Codella, Noel CF, et al. "Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)." 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018.

Gutman, David, et al. "Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC)." arXiv preprint arXiv:1605.01397 (2016).

Codella, Noel, et al. "Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic)." arXiv preprint arXiv:1902.03368 (2019).

Veronica, Rotemberg, et al. "A patient-centric dataset of images and metadata for identifying melanomas using clinical context." Scientific Data 8.1 (2021).

Marchetti, Michael A., et al. "Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images." Journal of the American Academy of Dermatology 78.2 (2018): 270-277.

Tschandl, Philipp, et al. "Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study." The Lancet Oncology 20.7 (2019): 938-947.

Tschandl, Philipp, et al. "Human–computer collaboration for skin cancer recognition." Nature Medicine 26.8 (2020): 1229-1234.

Rotemberg, Veronica, et al. "The role of public challenges and data sets towards algorithm development, trust, and use in clinical practice." Seminars in cutaneous medicine and surgery. Vol. 38. No. 1. 2019.

Workshops and Conferences

AI Challenges

To date we have hosted 5 ISIC challenges, which engaged thousands of participants from the computer science communities.

ISIC Archive

Our primary focus is the development of the ISIC Archive, a large and expanding open source public access archive of annotated skin images. The Archive currently contains over 100,000 images.

Accomplishments

Leader

Veronica Rotemberg, MD, PhD

Memorial Sloan Kettering Cancer Center

New York, NY, USA

Memorial Sloan Kettering Cancer Center

Veronica Rotemberg, MD, PhD

Leader

Working Group Members

Hospital Clinic of Barcelona, Spain

Barcelona, Spain

Josep Malvehy, MD

Royal Prince Alfred Hospital

Sydney, Australia

Pascale Guitera, MD

The University of Queensland

Brisbane, Australia

H. Peter Soyer, MD, PhD

Medical University of Vienna, Vienna, Austria

Vienna, Austria

Philipp Tschandl, MD, PhD

Institue for Systems and Robitics, Instituto Superio Tecnico

Lisbon, Portugal

Catarina Barata, PhD

The University of Queensland

Brisbane, Australia

Bridgid Betz-Stablein, PhD

University of Central Arkansas

Conway, AR, USA

M. Emre Celebi, PhD

Fundacio Clinic per a la Recerca Biomedia

Barcelona, Spain

Marc Combalia

Emory University

Atlanta, GA, USA

Roxana Daneshjou, MD, PhD

Emory University

Atlanta, GA, USA

David Gutman, MD PhD

Memorial Sloan Kettering Cancer Center

New York, NY, USA

Allan Halpern, MD, MSc

Kitware

Clifton Park, NY, USA

Brian Helba

Centre of Excellence in AI, AGH University of Science and Technology

Poland

Joanna Jaworek-Korjakowska, Univ. Prof.

Medical University of Vienna

Vienna, Austria

Harald Kittler, MD

Memorial Sloan Kettering Cancer Center

New York, NY, USA

Kivanc Kose, PhD

Memorial Sloan Kettering Cancer Center

New York, NY, USA

Nicholas R. Kurtansky

Andreas Sygros Hospital of Skin and Venereological Disease

Athens, Greece

Konstantinos Liopyris, MD

I Dermatology Clinical

Seoul, South Korea

Han Seung Seog, MD

Vanderbilt University Medical Center

Nashville, TN, USA

Eric Tkaczyk, MD, PhD

Memorial Sloan Kettering Cancer Center

New York, NY, USA

Jochen Weber

Department of Computing and Mathematics, Manchester Metropolitan University

Moi Hoon Yap, PhD

University Hosptial Zurich, Zurich, Switzerland

Zurich, Switzerland

Lara Valeska Maul, MD, FEBDV

Next Steps

Continue to develop additional functionality in the Archive platform and scale the size and complexity of the Archive.
Add clinical (standard photography) close-up images to the current collection of mostly dermoscopic images and increase the ethnic/racial and diagnosis diversity of the Archive.
Continue to crowd source the annotations of the images in the Archive for dermoscopy attributes, in partnership with the Education Working Group and with Centaur Labs.
Support and host Grand Challenges and Live Challenges.
Efforts are underway to assemble an annotated collection of images of lesions captured sequentially to permit inclusion of change-over-time as an additional diagnostic variable.
Explore multi-modal images (e.g., dermoscopy together with clinical +/- RCM +/- histology).
Engage the relevant communities with conferences and publications.
Among these are an upcoming workshop at CVPR and organizing special issues on skin imaging for Elsevier's Medical Image Analysis (MIA) journal and the Journal of Biomedical and Health Informatics.