Skin cancer can be deadly if not caught early, but many populations lack specialized dermatologic care. Over the past several years, dermoscopy-based AI algorithms have been shown to benefit clinicians in diagnosing melanoma, basal cell, and squamous cell carcinoma. However, determining which individuals should see a clinician in the first place has great potential impact. Triaging applications have a significant potential to benefit underserved populations and improve early skin cancer detection, the key factor in long-term patient outcomes.
This project is about developing image-based algorithms to identify histologically confirmed skin cancer cases with single-lesion crops from 3D total body photos (TBP). The image quality resembles close-up smartphone photos, which are regularly submitted for telehealth purposes.
This project is part of the ISIC 2024 - Skin Cancer Detection with 3D-TBP. To find more information about the competition, visit the ISIC 2024 - Skin Cancer Detection with 3D-TBP.
The dataset consists of diagnostically labelled images with additional metadata. The images are JPEGs. The associated .csv file contains a binary diagnostic label (target), potential input variables (e.g. age_approx, sex, anatom_site_general, etc.), and additional attributes (e.g. image source and precise diagnosis).
The datasets contains 15mm-by-15mm field-of-view cropped images, centered on distinct lesions, that were extracted from 3D total body photographs. The official training dataset for the challenge is the SLICE-3D Dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection. The official testing dataset contain tiles from a separate set of patients.
Datasets were curated by the International Skin Imaging Collaboration (ISIC) from images contributed from the following sources:
- Memorial Sloan Kettering Cancer Center (USA)
- Hospital Clinic de Barcelona (Spain)
- The University of Queensland (Australia)
- Medical University of Vienna (Austria)
- University of Athens (Greece)
- Melanoma Institute Australia (Australia)
- University Hospital of Basel (Switzerland)
- Alfred Hospital (Australia)
- FNQH Cairns (Australia)
SLICE-3D dataset is under CC BY-NC 4.0 with the following attribution:
International Skin Imaging Collaboration. SLICE-3D 2024 Challenge Dataset. International Skin Imaging Collaboration: https://doi.org/10.34970/2024-slice-3d(2024).
Creative Commons Attribution-Non Commercial 4.0 International License.
The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital ClÃnic de Barcelona, Memorial Sloan Kettering Cancer Center, Hospital of Basel, FNQH Cairns, The University of Queensland, Melanoma Institute Australia, Monash University and Alfred Health, University of Athens Medical School, and Medical University of Vienna.Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
This project is licensed under the Apache License, Version 2.0 - see the LICENSE file for details.
The data is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - see the DATA LICENSE for more details.
- ISIC 2024 - Skin Cancer Detection with 3D-TBP
- KerasCV Starter
- ISIC 2024 Kaggle
- International Skin Imaging Collaboration (ISIC)
- Memorial Sloan Kettering Cancer Center (USA)
- Hospital Clinic de Barcelona (Spain)
- The University of Queensland (Australia)
- Medical University of Vienna (Austria)
- University of Athens (Greece)
- Melanoma Institute Australia (Australia)
- University Hospital of Basel (Switzerland)
- Alfred Hospital (Australia)
- FNQH Cairns (Australia)
- Kaggle
@misc{isic-2024-challenge,
author = {Nicholas Kurtansky, Veronica Rotemberg, Maura Gillis, Kivanc Kose, Walter Reade, Ashley Chow},
title = {ISIC 2024 - Skin Cancer Detection with 3D-TBP},
publisher = {Kaggle},
year = {2024},
url = {https://kaggle.com/competitions/isic-2024-challenge}
}