Table 1:
Results on held-out test set. Weakly supervised ResNet performs on par with the fully supervised model and outperforms ResNet trained using active learning. FPR50% TPR and FNR50% TNR represent the FPR and FNR at 50% TPR and TNR, respectively. Similarly, TPR1% FPR and TNR1% FNR represent the TPR and TNR at 1% FPR and FNR, respectively. The reported AL results are averaged over 10 independent initializations of the random seed set. All measures are computed with PVC as the positive class.
| Model | TPR | TNR | PPV | FPR | Acc | FPR50% TPR | FNR 50% TNR | TPR 1% FPR | TNR 1% FNR |
|---|---|---|---|---|---|---|---|---|---|
| Fully sup. | 0.884 | 0.970 | 0.664 | 0.030 | 96.25 | 0.005 | 0.028 | 0.793 | 0.266 |
| Pr. labels | 0.645 | 0.960 | 0.523 | 0.039 | 85.84 | 0.019 | 0.140 | 0.165 | 0.252 |
| Active learn. | 0.514 | 0.993 | 0.821 | 0.007 | 94.15 | 0.020 | 0.021 | 0.604 | 0.405 |
| Weak sup. | 0.892 | 0.965 | 0.629 | 0.036 | 97.25 | 0.004 | 0.013 | 0.707 | 0.466 |