RSNA
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Object Detection | RSNA | mAP (%)31.1 | 106 | |
| Semantic Segmentation | RSNA | Dice Score76.9 | 90 | |
| Image Classification | RSNA (test) | AUC91 | 59 | |
| Anomaly Detection | RSNA Chest X-ray lung opacity (test) | Image-level AUROC99.2 | 57 | |
| Medical Image Classification | RSNA | AUC94.5 | 48 | |
| Classification | RSNA (test) | F1 Score84.8 | 44 | |
| Image Classification | RSNA | AUC90.8 | 42 | |
| Linear Classification | RSNA (test) | AUC90.8 | 39 | |
| Classification | RSNA | Accuracy85.13 | 38 | |
| Anomaly Detection | RSNA (test) | AUC99.6 | 30 | |
| Image Classification | RSNA | AUROC91.7 | 24 | |
| Classification | RSNA | AUC89.8 | 24 | |
| Anomaly Detection | RSNA | AU-ROC (Image-level, Det.)91 | 22 | |
| Binary disease diagnosis | RSNA OOD | Macro Accuracy79 | 21 | |
| Patient-level fracture detection | RSNA (patient-level) | Accuracy84.39 | 15 | |
| Top-k localization precision and sensitivity | RSNA | Top-k Precision73 | 14 | |
| Cancer Classification | RSNA Cancer | AUC92.5 | 13 | |
| Lesion Segmentation | RSNA 56 | Dice Score80.22 | 12 | |
| Medical Image Classification | RSNA | F1 Score95.79 | 12 | |
| Classification | RSNA X-ray | Accuracy74.53 | 11 | |
| Bone age assessment | RSNA | MAE (months)3.81 | 11 | |
| Chest X-ray opacity removal image translation | RSNA dataset 2019 | FID35.1 | 9 | |
| Classification | RSNA 56 (test) | F1 Score77.4 | 9 | |
| Vertebra Fracture Recognition | RSNA dataset (test) | Accuracy94.51 | 9 | |
| Linear classification | RSNA 100% (train) | AUC93.6 | 9 |
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