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.
CPTAC — Clinical Proteomic Tumor Analysis Consortium
Proteogenomic companion to TCGA: the largest public set where protein-level measurements, genomics and images exist for the same tumours.
PANDA — Prostate cANcer graDe Assessment
Reference dataset and challenge for AI Gleason grading; the follow-up Nature Medicine paper validated algorithms on external cohorts.
PatchCamelyon (PCam)
Small, fast, fully open patch-classification benchmark — the standard smoke test for image encoders in pathology and a common zero-shot evaluation set.
TCGA — The Cancer Genome Atlas (via NCI Genomic Data Commons)
The reference multi-omics cancer cohort: molecular profiles, clinical outcomes and whole-slide images for 33 cancer types, downloadable through the GDC portal and API.
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.