Datasets › HAM10000
HAM10000
HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.
Source: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000 Image Source: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Lesion Segmentation | HAM10000 | DermoSegDiff-B Dice Score 0.943 | DermoSegDiff: A Boundary-aware Segmentation Diffusion... | mindflow-institue/dermosegdiff | 1 | Compare |
| Semantic Segmentation | HAM10000 | MFSNet Average Dice 90.6 | MFSNet: A Multi Focus Segmentation Network for Skin... | rohit-kundu/mfsnet +1 | 1 | Compare |
Papers archive 2025-07-28
2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 209. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation | 1 | 1 | 5 Aug 2023 | not harvested |
| MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation | 2 | 1 | 27 Mar 2022 | not harvested |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- HAM10000
1 variant name, as the archive lists them.
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