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Medical Image Segmentation On Medical
Metrics
Dice (Average)
NSD
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| Swin UNETR | 78.68 | 89.28 | Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis | |
| DiNTS | 77.93 | 88.68 | DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation | |
| nnUNet | 77.89 | 88.09 | nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation | |
| Models Genesis | 76.97 | 87.19 | Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis | |
| Trans VW | 76.96 | 87.64 | Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning |
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