LA
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Medical Image Segmentation | LA | Dice91.82 | 97 | |
| Medical Image Segmentation | LA Atrial Segmentation Challenge 2018 (evaluation) | Dice91.47 | 75 | |
| Medical Image Segmentation | LA (val test) | Dice Coefficient91.47 | 17 | |
| Medical Image Segmentation | LA seven-shot 2018 | DICE86.22 | 16 | |
| Multi-step forecasting | LA | MSE0.7594 | 14 | |
| One-step forecasting | LA | MSE0.2621 | 14 | |
| 3D Medical Image Segmentation | LA dataset | Dice91.88 | 12 | |
| Medical Image Segmentation | LA (test) | Mean Dice89.22 | 12 | |
| Medical Image Segmentation | LA (20% labels) | Dice0.9054 | 11 | |
| 3D Medical Image Segmentation | LA 4 labeled scans 5% ratio v1 | Dice87.34 | 11 | |
| Medical Image Segmentation | LA (10% labels) | Dice Score88.78 | 10 | |
| Medical Image Segmentation | LA (MRI) (test) | DSC92.91 | 10 | |
| Segmentation | LA dataset MT-Noise (test) | Dice90.64 | 9 | |
| Segmentation | LA SFDA-Noise (test) | Dice88.65 | 9 | |
| Federated Clustering | LA | NMI0.576 | 9 | |
| Global Clustering | LA | Purity65.8 | 9 | |
| Clustering | LA | NMI53.5 | 9 | |
| Clustering | LA | Purity75.2 | 9 | |
| Global Clustering | LA | Silhouette Coefficient0.316 | 9 | |
| Segmentation | LA 20% labeled (test) | DSC91.23 | 8 | |
| Segmentation | LA 10% labeled data v1 (test) | DSC (%)0.9123 | 8 | |
| Medical Image Segmentation | LA 2018 | DICE55.03 | 6 | |
| Medical Image Segmentation | LA 50% labels | Dice Coefficient91.84 | 2 | |
| Medical Image Segmentation | LA (5% labels) | Dice Score (%)84.62 | 2 | |
| Medical Image Segmentation | LA | Metric- | 0 |
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