Table 5.
Comparison of pretrained weights: self-supervised learning with large non-medical images versus supervised learning with a large, task-specific chest radiograph dataset
| Labels | VinDr-CXR | ChestX-ray14 | CheXpert | UKA-CXR | PadChest | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| DINOv2 | MIMIC-CXR | DINOv2 | MIMIC-CXR | DINOv2 | MIMIC-CXR | DINOv2 | MIMIC-CXR | DINOv2 | MIMIC-CXR | |
| Cardiomegaly | 94.53 ± 0.52 | 97.17 ± 0.34 | 88.51 ± 0.47 | 89.54 ± 0.44 | 87.96 ± 0.31 | 87.27 ± 0.31 | 85.86 ± 0.18 | 85.45 ± 0.18 | 92.30 ± 0.27 | 92.68 ± 0.26 |
| Pleural effusion | 97.62 ± 0.68 | 98.31 ± 0.52 | 81.01 ± 0.32 | 82.00 ± 0.32 | 87.81 ± 0.20 | 87.64 ± 0.20 | 91.23 ± 0.19 | 91.41 ± 0.19 | 95.66 ± 0.26 | 95.85 ± 0.24 |
| Pneumonia | 91.99 ± 0.98 | 94.46 ± 0.66 | 70.17 ± 1.03 | 69.85 ± 1.04 | 76.42 ± 0.88 | 76.29 ± 0.84 | 92.15 ± 0.18 | 91.94 ± 0.18 | 83.93 ± 0.67 | 84.96 ± 0.66 |
| Atelectasis | 88.55 ± 1.71 | 92.21 ± 1.48 | 75.56 ± 0.43 | 75.87 ± 0.41 | 69.57 ± 0.40 | 69.28 ± 0.39 | 86.36 ± 0.23 | 86.30 ± 0.24 | 83.62 ± 0.58 | 83.59 ± 0.55 |
| Consolidation | 91.35 ± 1.56 | 94.82 ± 0.74 | 73.60 ± 0.57 | 75.11 ± 0.54 | 75.14 ± 0.56 | 74.13 ± 0.56 | N/A | N/A | 88.26 ± 0.82 | 89.95 ± 0.76 |
| Pneumothorax | 90.96 ± 2.91 | 97.39 ± 1.27 | 84.70 ± 0.38 | 85.93 ± 0.37 | 87.29 ± 0.33 | 86.03 ± 0.34 | N/A | N/A | 86.37 ± 2.01 | 92.89 ± 1.00 |
| Lung opacity | 86.86 ± 1.27 | 87.89 ± 1.26 | N/A | N/A | 73.98 ± 0.28 | 73.62 ± 0.29 | N/A | N/A | N/A | N/A |
| Lung lesion | N/A | N/A | N/A | N/A | 76.56 ± 0.73 | 75.79 ± 0.73 | N/A | N/A | N/A | N/A |
| Fracture | N/A | N/A | N/A | N/A | 77.93 ± 0.67 | 76.92 ± 0.66 | N/A | N/A | N/A | N/A |
| No finding (healthy) | 90.79 ± 0.56 | 93.51 ± 0.46 | 72.37 ± 0.33 | 72.48 ± 0.33 | 87.61 ± 0.30 | 87.53 ± 0.31 | 86.86 ± 0.18 | 86.49 ± 0.18 | 85.11 ± 0.26 | 85.20 ± 0.26 |
| Average | 91.58 ± 3.45 | 94.47 ± 3.30 | 77.99 ± 6.38 | 78.68 ± 6.77 | 80.03 ± 6.60 | 79.45 ± 6.60 | 88.49 ± 2.65 | 88.32 ± 2.77 | 87.89 ± 4.30 | 89.30 ± 4.45 |
| p-value | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | |||||
The table showcases area under receiver operating characteristic curve (ROC-AUC) percentages for each individual label across datasets: VinDr-CXR, ChestX-ray14, CheXpert, UKA-CXR, and PadChest. These datasets were pretrained using SSL on non-medical images (DINOv2) and fully supervised learning on a dedicated chest radiograph dataset (MIMIC-CXR). The total fine-tuning training images for VinDr-CXR, ChestX-ray14, CheXpert, UKA-CXR, and PadChest were n = 15,000, n = 86,524, n = 128,356, n = 153,537, and n = 88,480, respectively, with corresponding test images totals of n = 3,000, n = 25,596, n = 39,824, n = 39,824, and n = 22,045, respectively. p-values signify the comparison between the average ROC-AUCs from DINOv2 and MIMIC-CXR. For details about each dataset’s labels, refer to Table 3
N/A Not available