Datasets › ISIC 2019

ISIC 2019

archive 2025-07-28

The goal for ISIC 2019 is classify dermoscopic images among nine different diagnostic categories.25,331 images are available for training across 8 different categories. Two tasks will be available for participation: 1) classify dermoscopic images without meta-data, and 2) classify images with additional available meta-data.

Benchmarks archive 2025-07-28

All 3 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)PaperCode
Skin Lesion Classification ISIC 2019 Ensemble Accuracy 0.634 Analysis of skin lesion images with deep learning j05t/lesion-analysis 2 Compare
Classification ISIC 2019 CASS Balanced Multi-Class Accuracy 0.6519 CASS: Cross Architectural Self-Supervision for Medical... pranavsinghps1/CASS 1 Compare
Partial Label Learning ISIC 2019 CASS Balanced Multi-Class Accuracy 0.7258 CASS: Cross Architectural Self-Supervision for Medical... pranavsinghps1/CASS 1 Compare

Papers archive 2025-07-28

3 shown of 3 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 13. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • ISIC 2019

1 variant name, as the archive lists them.

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