RSNA
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Object Detection | RSNA | mAP (%)31.1 | 99 | |
| Semantic Segmentation | RSNA | Dice Score76.9 | 90 | |
| Image Classification | RSNA (test) | AUC91 | 49 | |
| Image Classification | RSNA | AUC90.8 | 42 | |
| Linear Classification | RSNA (test) | AUC90.8 | 39 | |
| Medical Image Classification | RSNA | AUC94.5 | 36 | |
| Classification | RSNA | Accuracy80.36 | 29 | |
| Image Classification | RSNA | AUROC91.7 | 24 | |
| Classification | RSNA (test) | Accuracy85.23 | 24 | |
| Classification | RSNA | AUC89.8 | 24 | |
| Anomaly Detection | RSNA | AU-ROC (Image-level, Det.)91 | 22 | |
| Patient-level fracture detection | RSNA (patient-level) | Accuracy84.39 | 15 | |
| Top-k localization precision and sensitivity | RSNA | Top-k Precision73 | 14 | |
| Medical Image Classification | RSNA | F1 Score95.79 | 12 | |
| Bone age assessment | RSNA | MAE (months)3.81 | 11 | |
| Vertebra Fracture Recognition | RSNA dataset (test) | Accuracy94.51 | 9 | |
| Linear classification | RSNA 100% (train) | AUC93.6 | 9 | |
| Linear classification | RSNA 10% (train) | AUC0.929 | 9 | |
| Linear classification | RSNA 1% (train) | AUC92.2 | 9 | |
| Classification | RSNA Linear Evaluation 100% ratio | Accuracy84.11 | 7 | |
| Classification | RSNA Linear Evaluation 10% ratio | Accuracy83.74 | 7 | |
| Classification | RSNA Linear Evaluation 1% ratio | Accuracy83.06 | 7 | |
| Object Detection | RSNA (test) | Recall @ IoU 0.0532 | 7 | |
| Head CT classification | RSNA 5-tasks | mAUC91.5 | 6 | |
| Vertebra Level Fracture Recognition | RSNA Dataset | Accuracy0.9975 | 6 |
Showing 25 of 30 rows