Global open skin photo platform to improve automated skin cancer detection
Official title: M-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer Detection
Funded at Sloan-Kettering Inst Can Research · NIH-11178369 · Data updated 2026-11-12
This project builds a worldwide, open collection of skin photos and tools to help AI and clinicians better spot skin cancer from images.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Sloan-Kettering Inst Can Research NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 1, 2022 |
| Project end | August 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $808,749 |
| NIH project number | 5U24CA264369-04 |
| Project ID | NIH-11178369 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal contributors are people with suspicious moles or skin lesions, those with a history of skin cancer, or anyone willing to share clinical photos and related information for research.
✕Less likely to help
People who need immediate treatment or who cannot or will not share photos or clinical data are unlikely to receive direct benefit from this platform itself.
What this research studies
From a patient point of view, this project collects and standardizes skin photos and tied clinical information into a single, open archive so images from many clinics and devices can be used together. The team will create automated pipelines to ingest, organize, register, and annotate images so algorithms see consistent, high-quality data. Open-source tools and the expanded International Skin Imaging Collaboration (ISIC) archive will support AI developers and clinician training, and contributions can be made remotely. The effort aims to make automated detection more reliable across different cameras, settings, and patient skin types.
Why it matters
Potential benefit: If successful, patients could get more accurate and broadly applicable image-based screening tools and better-trained clinicians, improving early detection of skin cancers like melanoma.
How similar studies have gone: Previous ISIC challenges and studies have shown AI can match or exceed clinician performance on static images, but standardized multimodal datasets and cross-device performance remain less proven.
Where this research is happening
-
Sloan-Kettering Inst Can Research
New York, United States Active
Researchers
- Principal investigator: Rotemberg, Veronica Miriam — Sloan-Kettering Inst Can Research
- Study coordinator: Rotemberg, Veronica Miriam
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.