Cancer
Datasets
All datasets matching “Cancer”CancerVerse
🩻 CancerVerse
The first longitudinal, multimodal CT dataset spanning 13 malignant tumor types
CancerVerse pairs whole-body abdominal/pelvic CT volumes with the radiologists' own free-text reports, follows patients across time, and is being released in stages — culminating in expert voxel-level tumor masks and a deep clinical & longitudinal annotation layer. It is built to power the next generation of cancer-aware medical AI: tumor detection and segmentation… See the full description on the dataset page: https://huggingface.co/datasets/BodyMaps/CancerVerse.skin-cancer-ham10000
Skin Cancer: HAM10000
Skin Cancer: HAM10000 is a dataset for semantic segmentation task.
skin_cancer
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Original Paper and Dataset here
Kaggle dataset here
Introduction to datasets
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset.… See the full description on the dataset page: https://huggingface.co/datasets/marmal88/skin_cancer.state-cancer-profiles
United States State Cancer Profiles data extract (mirror)
This is a mirror. Cite the Zenodo record, not this page:
Davis S. United States State Cancer Profiles data extract — vintage V3. Zenodo. https://doi.org/10.5281/zenodo.22085273
Concept DOI (always resolves to the latest vintage): https://doi.org/10.5281/zenodo.11098814
No DOI is minted on Hugging Face. HF hosts these bytes for native hf:// / DuckDB access and an ML audience that would never find the Zenodo record;… See the full description on the dataset page: https://huggingface.co/datasets/seandavis/state-cancer-profiles.skin-cancer-ham10000-datasetbreast-cancer-wisconsin
Breast Cancer Wisconsin Diagnostic Dataset
Following description was retrieved from breast cancer dataset on UCI machine learning repository.
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at here.
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin.