CH
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Subtype diagnosis | CH dataset | AUC0.942 | 80 | |
| Benign-malignant classification | CH dataset | AUC94.2 | 80 | |
| Tumor screening | CH | AUC0.99 | 80 | |
| Tumor segmentation | CH dataset | Dice89.9 | 75 | |
| Binary Classification | CH (test) | Accuracy95.2 | 64 | |
| Regression | CH | Negative RMSE-0.419 | 29 | |
| Feature Selection | CH | Precision100 | 17 | |
| Feature Selection | CH | ROC-AUC100 | 17 | |
| 3D Super-Resolution | CH (test) | SSIM0.8926 | 10 | |
| Automatic Speech Recognition | CH | WER0.2448 | 9 | |
| Classification | CH | Accuracy86.2 | 7 | |
| Tabular Prediction | CH | Score0.864 | 7 | |
| Tabular Classification | CH | Macro F175.2 | 6 | |
| Tabular Data Generation | CH | MLE0.702 | 6 | |
| 2D Super-Resolution | CH 2x upscaling (test) | SSIM0.9291 | 6 | |
| Regression | CH with second-order extra features | Negative RMSE-0.422 | 5 | |
| 3D Super-Resolution | CH 4x upscaling (test) | SSIM78.19 | 5 | |
| 3D Super-Resolution | CH 2x upscaling (test) | SSIM0.8926 | 5 | |
| 2D Super-Resolution | CH 4x upscaling (test) | SSIM83.08 | 5 | |
| Abusive language detection | CH (test) | F1 Score73 | 4 |
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