Mutation Prediction on TCGA-CRC
94AUCautol
Evaluation results
| Method | Links | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| autolmulti_task=true, weighting_strategy=autol2024.03 | 94 | — | — | — | — | — | — | — | — | — | — | 84.5 | |
| uncert + pcgradmulti_task=true, weighting_strategy=uncertainty, gradient_strategy=pcgrad2024.03 | 86.7 | — | — | — | — | — | — | — | — | — | — | 62.5 | |
| cagradmulti_task=true, gradient_strategy=cagrad2024.03 | 86.5 | — | — | — | — | — | — | — | — | — | — | 62.7 | |
| autol + cagradmulti_task=true, weighting_strategy=autol, gradient_strategy=cagrad2024.03 | 86.5 | — | — | — | — | — | — | — | — | — | — | 62 | |
| naivemulti_task=true, weighting_strategy=naive2024.03 | 86.4 | — | — | — | — | — | — | — | — | — | — | 62.7 | |
| uncert + cagradmulti_task=true, weighting_strategy=uncertainty, gradient_strategy=cagrad2024.03 | 86.3 | — | — | — | — | — | — | — | — | — | — | 60.7 | |
| baselinemulti_task=false2024.03 | 86.1 | — | — | — | — | — | — | — | — | — | — | 61.4 | |
| autol + pcgradmulti_task=true, weighting_strategy=autol, gradient_strategy=pcgrad2024.03 | 86.1 | — | — | — | — | — | — | — | — | — | — | 63.2 | |
| uncertmulti_task=true, weighting_strategy=uncertainty2024.03 | 86 | — | — | — | — | — | — | — | — | — | — | 63.4 | |
| graddropmulti_task=true, gradient_strategy=graddrop2024.03 | 85.8 | — | — | — | — | — | — | — | — | — | — | 61.2 | |
| dwa + cagradmulti_task=true, weighting_strategy=dwa, gradient_strategy=cagrad2024.03 | 85.8 | — | — | — | — | — | — | — | — | — | — | 59.9 | |
| dwa + pcgradmulti_task=true, weighting_strategy=dwa, gradient_strategy=pcgrad2024.03 | 85.6 | — | — | — | — | — | — | — | — | — | — | 62.3 | |
| dwa + graddropmulti_task=true, weighting_strategy=dwa, gradient_strategy=graddrop2024.03 | 85.5 | — | — | — | — | — | — | — | — | — | — | 59.8 | |
| uncert + graddropmulti_task=true, weighting_strategy=uncertainty, gradient_strategy=graddrop2024.03 | 85.5 | — | — | — | — | — | — | — | — | — | — | 60.7 | |
| pcgradmulti_task=true, gradient_strategy=pcgrad2024.03 | 85.4 | — | — | — | — | — | — | — | — | — | — | 62.4 | |
| autol + graddropmulti_task=true, weighting_strategy=autol, gradient_strategy=graddrop2024.03 | 85.3 | — | — | — | — | — | — | — | — | — | — | 61.6 | |
| dwamulti_task=true, weighting_strategy=dwa2024.03 | 84.2 | — | — | — | — | — | — | — | — | — | — | 58.7 | |
| SOTA2024.03 | 83 | — | — | — | — | — | — | — | — | — | — | — | |
| Fu et al. [6]Prediction level=tile-wise average pooling, Training=single model per target2022.05 | — | — | — | 57 | — | — | 66 | 55 | 59 | 58 | 68 | — | |
| Kather et al. [10]Prediction level=instance-wise2022.05 | — | 77 | — | — | — | — | — | — | — | — | — | — | |
| Kather et al. [9]Prediction level=tile-wise average pooling, Training=single model per target2022.05 | — | — | — | 66 | 51 | — | 49 | 60 | 62 | 63 | 68 | — | |
| LA-MILAttention=local self-attention, Prediction level=WSI-level, Training=multi-target model2022.05 | — | 85 | 0.83 | 72 | 63 | 60 | 66 | 62 | 61 | 58 | 63 | — | |
| T-MILAttention=global self-attention, Prediction level=WSI-level, Training=multi-target model2022.05 | — | 85 | 0.82 | 73 | 61 | 57 | 64 | 61 | 60 | 60 | 64 | — | |
| Wang et al. [22]Prediction level=instance-wise2022.05 | — | — | 0.82 | — | — | — | — | — | — | — | — | — |