Table 3:

The performance varies with pooling strategies for MIL. Attention and Max Pooling (AMP) combines the output of Max Pooling (MP) and Attention Pooling (AP). MIL utilized the feature extracted by the model trained for image-level PE classification. For all three architectures, the best mean AUC is obtained by AMP, highlighting the importance of combining AP and MP.

Architecture	SeResNeXT50			Xception			SeXception
Labels/Pooling	AMP	MP	AP	AMP	MP	AP	AMP	MP	AP
NegExam PE	0.9138	0.9137	0.9188	0.9202	0.9202	0.9172	0.9183	0.9137	0.9201
Indetermine	0.9144	0.9064	0.8986	0.8793	0.8580	0.8933	0.8616	0.8564	0.8499
Left PE	0.9122	0.9059	0.9086	0.9106	0.9100	0.9032	0.9042	0.9004	0.9024
Right PE	0.9340	0.9345	0.9373	0.9397	0.9366	0.9397	0.9403	0.9383	0.9412
Central PE	0.9561	0.9537	0.9529	0.9487	0.9465	0.9507	0.9472	0.9424	0.9453
RV LV Ratio≥1	0.8813	0.8774	0.8822	0.8920	0.8871	0.8819	0.8827	0.8779	0.8813
RV LV Ratio<1	0.8597	0.8606	0.862	0.8644	0.8619	0.8567	0.8676	0.8642	0.8644
Chronic PE	0.7304	0.7256	0.7233	0.7788	0.7664	0.7699	0.7334	0.7168	0.7342
Acute&Chronic PE	0.8453	0.8470	0.8228	0.8392	0.8396	0.8350	0.8405	0.8341	0.8367
Mean AUC	0.8830	0.8805	0.8785	0.8859	0.8807	0.8831	0.8773	0.8716	0.8751