AI in Oncology · AI Models
A researcher-grade catalog of AI models, datasets and open data needs in oncology — every card structured, sourced and dated.
Datasets, registries and benchmarks you can train or evaluate oncology models on — with access conditions, licences, sizes, annotations and the models already using them. { } export JSON
CAMELYON16 / CAMELYON17
The canonical whole-slide benchmark for breast cancer lymph-node metastasis detection; still the first sanity check for any new pathology encoder.
Most-used open mammography set with pathology-confirmed labels; film-based, so domain shift to modern digital mammography must be handled.
ISIC Archive — International Skin Imaging Collaboration
The public backbone of skin-cancer AI; strongly skewed towards light skin tones, which every model card built on it should say.
LIDC-IDRI — Lung Image Database Consortium
The standard open CT dataset for lung nodule detection and characterisation; basis of the LUNA16 challenge.
NLST — National Lung Screening Trial
The trial that established LDCT screening; its images plus outcomes trained Sybil and remain the only large public-by-application longitudinal LDCT cohort.
TCIA — The Cancer Imaging Archive
The main public archive of de-identified cancer imaging, organised into collections by disease and modality; the source of most public radiology training data in oncology.
Cards follow the cancer3.ai model-card standard: AI_MODEL_CARD_STANDARD.md. Corrections and new entries: contact the editorial team; every fact needs a public source.
This page is educational — it is not medical advice and does not replace consultation with an oncologist. Diagnostic and treatment decisions are made solely by specialist physicians.