A Kaggle competition where winning algorithms are open source to improve diagnostic accuracy of clinical dermatologists.

Leesburg, VA (May 27, 2020) – The Society for Imaging Informatics in Medicine (SIIM) and the International Skin Imaging Collaboration (ISIC) are working together to host a Kaggle Machine Learning Challenge on Melanoma Classification, using the ISIC Archive which contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions. Image contributors include: Hospital Clínic de Barcelona, Spain; Memorial Sloan Kettering Cancer Center New York; Medical University of Vienna, Austria; Melanoma Institute Australia; The University of Queensland, Australia, and the University of Athens Medical School.

“Building from last year’s Pneumothorax challenge for Radiology images, SIIM is excited to partner with ISIC in this year’s Dermatology Challenge. Once again, we are encouraging participants to use Healthcare relevant IT standards (DICOMweb and FHIR) to obtain data and report results” said Steven G. Langer, PhD, CIIP, FSIIM, Professor of Radiologic Physics and Imaging Informatics at Mayo Clinic and Co-chair of the SIIM Machine Learning Committee.

“The International Skin Imaging Collaboration (ISIC) is delighted to collaborate with SIIM for its fifth annual grand challenge for skin cancer identification. This year’s challenge will test the diagnostic impact of clinical context by providing multiple images of different lesions from individual patients. It will also be the first time we have incorporated data standards by using DICOM to encode the images” said Veronica Rotemberg, MD, PhD, Assistant Attending Dermatologist, Director of Imaging and Informatics at Memorial Sloan Kettering Cancer Center.

Challenge participants will develop image analysis tools to enable the automated diagnosis of melanoma using patient-level contextual information, a process more similar to a clinical workflow. Standards-based healthcare APIs will be used to reduce the interoperability barriers to clinical implementation post-competition.

Melanoma is a deadly disease, but if caught early, most melanomas can be cured with minor surgery. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists’ diagnostic accuracy. Better detection of melanoma has the opportunity to positively impact millions of people.

SIIM and ISIC will kick off the Melanoma Classification Challenge in the weeks leading up to SIIM 2020 Virtual Meeting, June 24-26 on SIIMU, the society’s online learning platform, and announce the winning teams in conjunction with the 2020 Virtual Conference on Machine Intelligence in Medical Imaging (C-MIMI), co-sponsored by the American Association of Physicists in Medicine (AAPM), September 13-14 on SIIMU.

The winning teams will be invited to present at a subsequent grand challenge workshop as part of MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, October 4-8, 2020 in Lima, Peru.

About the Society for Imaging Informatics in Medicine
The Society for Imaging Informatics in Medicine (SIIM) is the leading healthcare professional organization for those interested in the current and future use of informatics in medical imaging. The society’s mission is to advance medical imaging informatics across the enterprise through education, research, and innovation in a multi-disciplinary community.

About the International Skin Imaging Collaboration
The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions.

Contact:
For SIIM: Anna Zawacki, azawacki@siim.org
For ISIC: Veronica Rotemberg, rotembev@mskcc.org

Publish date

May 27, 2020

Topic

  • Industry Partners

Media Type

  • Press Release

Audience Type

  • Clinician
  • Developer
  • Imaging IT
  • Researcher/Scientist

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