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. 2019 Apr 23;9:6381. doi: 10.1038/s41598-019-42294-8

Table 4.

AUC result overview for all our experiments.

Pathology Without non-image features With non-image features
OTS FT 1channel large OTS FT 1channel large
Cardiomegaly 72.7 ± 1.8 88.5 ± 0.7 88.9 ± 0.5 89.7 ± 0.3 75.9 ± 1.4 88.4 ± 0.8 90.2 ± 0.4 89.8 ± 0.8
Emphysema 77.8 ± 2.1 89.2 ± 1.0 87.0 ± 0.8 88.3 ± 1.3 79.8 ± 1.9 89.4 ± 1.2 87.4 ± 1.3 89.1 ± 1.2
Edema 84.4 ± 0.6 89.1 ± 0.4 89.1 ± 0.6 88.8 ± 0.5 85.7 ± 0.5 89.1 ± 0.7 89.0 ± 0.6 88.9 ± 0.3
Hernia 78.8 ± 1.4 85.5 ± 3.8 88.1 ± 4.2 87.5 ± 4.5 81.9 ± 2.5 88.2 ± 3.2 89.3 ± 4.4 89.6 ± 4.4
Pneumothorax 77.3 ± 1.3 87.0 ± 0.8 85.7 ± 0.9 85.9 ± 0.9 79.1 ± 1.2 86.5 ± 0.6 85.4 ± 0.7 85.9 ± 1.1
Effusion 79.4 ± 0.4 87.1 ± 0.2 87.6 ± 0.2 87.6 ± 0.2 80.6 ± 0.4 87.2 ± 0.3 87.6 ± 0.2 87.3 ± 0.3
Mass 66.8 ± 0.6 82.2 ± 1.0 83.3 ± 0.6 83.9 ± 0.9 68.6 ± 0.6 82.2 ± 1.0 83.3 ± 0.7 83.2 ± 0.3
Fibrosis 72.0 ± 0.9 80.0 ± 0.9 79.9 ± 0.8 79.2 ± 1.6 73.9 ± 0.8 80.0 ± 0.9 79.6 ± 0.5 78.9 ± 0.5
Atelectasis 71.8 ± 0.6 80.3 ± 0.7 79.9 ± 0.4 79.2 ± 0.7 73.2 ± 0.7 80.1 ± 0.6 79.3 ± 0.6 79.1 ± 0.4
Consolidation 74.3 ± 0.3 79.5 ± 0.5 80.6  ±  0.4 80.0 ± 0.3 75.3 ± 0.3 79.6 ± 0.5 80.4 ± 0.5 80.0 ± 0.7
Pleural Thicken. 68.8 ± 1.0 79.0 ± 0.7 78.4 ± 0.9 78.0 ± 1.1 70.8 ± 1.1 78.6 ± 1.1 78.2 ± 1.3 77.1 ± 1.3
Nodule 65.0 ± 0.8 72.6 ± 0.9 73.3 ± 0.8 75.1 ± 1.3 66.5 ± 0.7 74.7 ± 0.6 74.0 ± 0.7 75.8 ± 1.4
Pneumonia 66.4 ± 2.7 74.4 ± 1.6 74.3 ± 1.5 75.3 ± 2.2 68.3 ± 2.3 73.3 ± 1.3 74.8 ± 1.5 76.7 ± 1.5
Infiltration 65.9 ± 0.2 69.9 ± 0.6 70.2 ± 0.3 70.2 ± 0.5 67.0 ± 0.4 70.2 ± 0.2 70.1 ± 0.5 70.0 ± 0.7
Average 73.0 ± 1.1 81.7 ± 1.0 81.9 ± 0.9 82.1 ± 1.2 74.8 ± 1.1 82.0 ± 0.9 82.0 ± 1.0 82.2 ± 1.1
No Findings 71.6 ± 0.3 76.9 ± 0.5 77.3 ± 0.3 77.1 ± 0.4 72.5 ± 0.3 76.8 ± 0.4 77.1 ± 0.4 77.1 ± 0.3

In this table, we present averaged results over all five splits and the calculated standard deviation (std) for each pathology. We divide our experiments into three categories. First, without and with non-image features. Second, transfer-learning with off-the-shelf (OTS) and fine-tuned (FT) models. Third, from scratch where “1channel” refers to same input size as in transfer-learning but changed number of channels. “large” means we changed the input dimensions to 448 × 448 × 1. For better comparison, we present the average AUC and the standard deviation over all pathologies in the last row. Bold text emphasizes the overall highest AUC value. Values are scaled by 100 for convenience.