{"count":6,"items":[{"access":"open","article":null,"cancer_slugs":["invasive-breast-carcinoma"],"doi":null,"formats":["TIFF","CSV"],"hf":null,"huggingface":null,"kind":"benchmark","license":"CC0","modalities":["histopathology"],"name":"CAMELYON16 / CAMELYON17","page":"/ai-oncology/datasets/camelyon16","provider":"Radboud University Medical Center and partners (grand-challenge.org)","size":{"items":1399,"notes_en":"CAMELYON16: 399 slides (270 train / 129 test) with pixel-level metastasis annotations; CAMELYON17: 1,000 slides from 5 centres with patient-level pN stage","notes_pl":"CAMELYON16: 399 preparat\u00f3w (270 tren. / 129 test.) z adnotacjami przerzut\u00f3w na poziomie pikseli; CAMELYON17: 1000 preparat\u00f3w z 5 o\u015brodk\u00f3w ze stadium pN per pacjent","unit":"WSIs"},"slug":"camelyon16","summary":"The canonical whole-slide benchmark for breast cancer lymph-node metastasis detection; still the first sanity check for any new pathology encoder.","tasks":["detection","classification","segmentation"],"url":"https://camelyon17.grand-challenge.org/","verified_at":"2026-09-05T22:25:58.842206"},{"access":"open","article":null,"cancer_slugs":["invasive-breast-carcinoma"],"doi":"10.7937/K9/TCIA.2016.7O02S9CY","formats":["DICOM","CSV"],"hf":null,"huggingface":null,"kind":"dataset","license":"CC BY 3.0","modalities":["mammography"],"name":"CBIS-DDSM \u2014 Curated Breast Imaging Subset of DDSM","page":"/ai-oncology/datasets/cbis-ddsm","provider":"TCIA","size":{"items":2620,"notes_en":"digitised film mammograms with verified pathology","notes_pl":"zdigitalizowane mammografie filmowe z potwierdzon\u0105 patomorfologi\u0105","patients":1566,"unit":"mammography studies"},"slug":"cbis-ddsm","summary":"Most-used open mammography set with pathology-confirmed labels; film-based, so domain shift to modern digital mammography must be handled.","tasks":["detection","classification"],"url":"https://www.cancerimagingarchive.net/collection/cbis-ddsm/","verified_at":"2026-09-05T22:25:58.952228"},{"access":"open","article":null,"cancer_slugs":["pan-cancer"],"doi":null,"formats":["DICOM","NIfTI","CSV"],"hf":null,"huggingface":null,"kind":"registry","license":"mostly CC BY 3.0/4.0 per collection; some restricted collections","modalities":["radiology-ct","radiology-mri","pet","mammography","histopathology","radiology-xray"],"name":"TCIA \u2014 The Cancer Imaging Archive","page":"/ai-oncology/datasets/tcia","provider":"NCI Cancer Imaging Program; hosted by the University of Arkansas for Medical Sciences","size":{"items":200,"notes_en":"hundreds of collections, tens of thousands of patients; DICOM with linked clinical and sometimes genomic data","notes_pl":"setki kolekcji, dziesi\u0105tki tysi\u0119cy pacjent\u00f3w; DICOM z powi\u0105zanymi danymi klinicznymi, czasem genomowymi","unit":"collections"},"slug":"tcia","summary":"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.","tasks":["segmentation","detection","classification","prognosis"],"url":"https://www.cancerimagingarchive.net/","verified_at":"2026-09-05T22:25:58.836069"},{"access":"open","article":null,"cancer_slugs":["lung-bronchus"],"doi":"10.7937/K9/TCIA.2015.LO9QL9SX","formats":["DICOM","JSON"],"hf":null,"huggingface":null,"kind":"dataset","license":"CC BY 3.0","modalities":["radiology-ct"],"name":"LIDC-IDRI \u2014 Lung Image Database Consortium","page":"/ai-oncology/datasets/lidc-idri","provider":"NCI / TCIA","size":{"items":1018,"notes_en":"nodule annotations by four thoracic radiologists in a two-phase reading","notes_pl":"adnotacje guzk\u00f3w przez czterech radiolog\u00f3w w dwufazowym odczycie","patients":1010,"unit":"CT scans"},"slug":"lidc-idri","summary":"The standard open CT dataset for lung nodule detection and characterisation; basis of the LUNA16 challenge.","tasks":["detection","segmentation","classification"],"url":"https://www.cancerimagingarchive.net/collection/lidc-idri/","verified_at":"2026-09-05T22:25:58.902160"},{"access":"controlled","article":null,"cancer_slugs":["lung-bronchus"],"doi":null,"formats":["DICOM","CSV"],"hf":null,"huggingface":null,"kind":"dataset","license":"NCI CDAS data-use agreement","modalities":["radiology-ct","radiology-xray"],"name":"NLST \u2014 National Lung Screening Trial","page":"/ai-oncology/datasets/nlst","provider":"NCI (Cancer Data Access System)","size":{"notes_en":"randomised trial of low-dose CT vs chest X-ray screening (2002\u20132009) with cancer and mortality follow-up; imaging available for a subset","notes_pl":"randomizowane badanie skriningu niskodawkowym TK vs RTG (2002\u20132009) z obserwacj\u0105 zachorowa\u0144 i zgon\u00f3w; obrazy dost\u0119pne dla podzbioru","patients":53454},"slug":"nlst","summary":"The trial that established LDCT screening; its images plus outcomes trained Sybil and remain the only large public-by-application longitudinal LDCT cohort.","tasks":["risk-prediction","screening","detection"],"url":"https://cdas.cancer.gov/nlst/","verified_at":"2026-09-05T22:25:58.927519"},{"access":"open","article":null,"cancer_slugs":["malignant-melanoma","non-melanoma-skin-cancer-nmsc"],"doi":null,"formats":["JPEG","CSV"],"hf":null,"huggingface":null,"kind":"registry","license":"CC-0 / CC BY-NC per contributor","modalities":["dermoscopy"],"name":"ISIC Archive \u2014 International Skin Imaging Collaboration","page":"/ai-oncology/datasets/isic-archive","provider":"ISIC (Memorial Sloan Kettering and partners)","size":{"items":70000,"notes_en":"tens of thousands of images with diagnosis; annual challenge subsets (e.g. 2020: 33,126 images)","notes_pl":"dziesi\u0105tki tysi\u0119cy obraz\u00f3w z rozpoznaniem; coroczne podzbiory konkursowe (np. 2020: 33 126 obraz\u00f3w)","unit":"dermoscopic images"},"slug":"isic-archive","summary":"The public backbone of skin-cancer AI; strongly skewed towards light skin tones, which every model card built on it should say.","tasks":["classification","detection","segmentation"],"url":"https://www.isic-archive.com/","verified_at":"2026-09-05T22:25:58.972520"}]}