Multi-Instance Learning TPAUC Maximization on Colon (test)
0.916Mean TPAUCSONT (att)
Evaluation Results
| Method | Links | |
|---|---|---|
| SONT (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.916 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.911 | |
| SONT (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.875 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=stochastic smoothed-max2023.10 | 0.863 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.862 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.826 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.1, 0.9), Pooling strategy=attention-based2023.10 | 0.803 | |
| SONT (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.8 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=stochastic smoothed-max2023.10 | 0.787 | |
| SOTAS (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.772 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.739 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.3, 0.7), Pooling strategy=attention-based2023.10 | 0.738 | |
| MIDAM (smx)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=stochastic smoothed-max2023.10 | 0.646 | |
| AUC-M (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.576 | |
| MIDAM (att)(TPR lower bound, FPR upper bound)=(0.5, 0.5), Pooling strategy=attention-based2023.10 | 0.548 |