Table 2:
The features extracted by the models trained for image-level classification were helpful for exam-level classification. However, no model performed consistently best for all labels. We report the mean AUC over 10 runs and bold the optimal results for each label.
| Labels | SeResNext50† | Xception | SeXception | DenseNet121 | ResNet18 | ResNet50 |
|---|---|---|---|---|---|---|
| NegExam PE | 0.9137 | 0.9242 | 0.9261 | 0.9168 | 0.9141 | 0.9061 |
| Indetermine | 0.8802 | 0.9168 | 0.8857 | 0.9233 | 0.9014 | 0.9278 |
| Left PE | 0.9030 | 0.9119 | 0.9100 | 0.9120 | 0.9000 | 0.8965 |
| Right PE | 0.9368 | 0.9419 | 0.9455 | 0.9380 | 0.9303 | 0.9254 |
| Central PE | 0.9543 | 0.9500 | 0.9487 | 0.9549 | 0.9445 | 0.9274 |
| RV LV Ratio≥1 | 0.8902 | 0.8924 | 0.8901 | 0.8804 | 0.8682 | 0.8471 |
| RV LV Ratio<1 | 0.8630 | 0.8722 | 0.8771 | 0.8708 | 0.8688 | 0.8719 |
| Chronic PE | 0.7254 | 0.7763 | 0.7361 | 0.7460 | 0.6995 | 0.6810 |
| Acute&Chronic PE | 0.8598 | 0.8352 | 0.8473 | 0.8492 | 0.8287 | 0.8398 |
| Mean AUC | 0.8807 | 0.8912 | 0.8852 | 0.8879 | 0.8728 | 0.8692 |
The Xception architecture achieved a significant improvement (p = 5.34E-12) against the previous state of the art†.