TN3K
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Medical Image Segmentation | TN3K (test) | Dice Score84.17 | 30 | |
| Object Detection | TN3K | AP54 | 19 | |
| Anomaly Localization | TN3K | AUROC89 | 19 | |
| Object size area estimation | Nodule TN3K (100 random splits) | Interval Size7,481 | 18 | |
| Ultrasound Image Segmentation | TN3K (test) | Dice83.43 | 17 | |
| Medical Grounding | TN3K | A@0.568.01 | 16 | |
| Medical Image Segmentation | TN3K 100 samples (train) | DICE66.16 | 16 | |
| Medical Image Segmentation | TN3K 25 samples (train) | DICE Score60.13 | 16 | |
| Ultrasound Image Segmentation | TN3K (unseen) | Dice62.09 | 14 | |
| Thyroid Ultrasound Segmentation | TN3K | DSC86.01 | 13 | |
| Medical Image Segmentation | TN3K | DSC88.9 | 13 | |
| Segmentation | TN3K (fold 5) | Dice Score75.06 | 10 | |
| Medical Image Segmentation | TN3K 1.0 (test) | mDice84.43 | 9 | |
| Thyroid Nodule Segmentation | TN3K 37 | DSC86.94 | 8 | |
| Anomaly Segmentation | TN3K | AUROC81.5 | 6 | |
| Pixel-level Anomaly Detection | TN3K | AUROC77.2 | 6 | |
| Thyroid nodule classification | TN3K (test) | Accuracy82.08 | 5 | |
| Medical Image Segmentation | TN3K 23 labeled samples (1%) | Dice Score65.07 | 5 | |
| Segmentation | TN3K | DSC87.05 | 4 | |
| Ultrasound Image Segmentation | TN3K | Dice Coefficient76.24 | 4 | |
| Medical Image Segmentation | TN3K (external) | Dice40.8 | 2 |
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