HAM10000
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Skin lesion classification | HAM10000 (test) | Accuracy93.32 | 83 | |
| Medical Image Classification | HAM10000 | Accuracy83.4 | 39 | |
| Medical Image Segmentation | HAM10000 | mDSC0.9471 | 27 | |
| Image Denoising | HAM10000 (test) | SSIM0.95 | 20 | |
| Image Denoising | HAM10000 52 (test) | PSNR38.8 | 20 | |
| Medical Image Classification | HAM10000 (random) | Accuracy83.3 | 19 | |
| Image Classification | HAM10000 | Accuracy97.1 | 19 | |
| Medical Grounding | HAM10000 | A@0.597.2 | 16 | |
| Classification | HAM10000 | AUC97.96 | 16 | |
| Attribution Analysis | HAM10000 | SEG-GRAD0.1726 | 13 | |
| Skin Lesion Segmentation | HAM10000 | Dice Coefficient95.01 | 12 | |
| Categorical skin lesion classification | HAM10000 original (test) | Accuracy0.8915 | 12 | |
| Medical Image Classification | HAM10000 D5 (test) | Accuracy99.8 | 12 | |
| Imbalanced Classification | HAM10000 (test) | Macro F1 Score81.97 | 10 | |
| Classification | HAM10000 (cross-validation) | Macro F158.55 | 9 | |
| Misclassification Detection | HAM10000 | AUC85.5 | 9 | |
| Skin lesion classification | HAM10000 | Accuracy88 | 9 | |
| Skin Cancer Classification | HAM10000 2018 | Group 0 Score71.2 | 9 | |
| Skin Lesion Classification | HAM10000 | Accuracy96.32 | 9 | |
| Learning to Defer | HAM10000 ID | SAC86 | 8 | |
| Skin lesion classification | HAM10000 (test) | AUC0.97 | 8 | |
| Medical Image Classification | HAM10000 (test) | AUC0.952 | 7 | |
| Classification | HAM10000 D5 | PGD Accuracy65.6 | 7 | |
| OOD Detection | HAM10000 (test) | AUC1 | 7 | |
| Classification | HAM10000 Linear Evaluation 100% ratio | Accuracy77.89 | 7 |
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