| Interactive AI-SAM gt box |
- Avg DSC
- 90.66
|
AI-SAM: Automatic and Interactive Segment Anything Model 2023-12-05 |
코드 1 |
| Medical SAM Adapter |
- Avg DSC
- 89.80
|
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation 2023-04-25 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 |
| MedSegDiff-v2 |
- Avg DSC
- 89.50
|
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer 2023-01-19 |
코드 1 · 코드 2 |
| nnUNet |
- Avg DSC
- 88.80
- Avg HD
- 10.78
|
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation 2018-09-27 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 |
| MedNeXt-L (5x5x5) |
- Avg DSC
- 88.76
|
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation 2023-03-17 |
코드 1 |
| MIST |
- Avg DSC
- 86.92
- Avg HD
- 11.07
|
MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder 2023-10-30 |
코드 1 |
| nnFormer |
- Avg DSC
- 86.57
- Avg HD
- 10.63
|
nnFormer: Interleaved Transformer for Volumetric Segmentation 2021-09-07 |
코드 1 · 코드 2 |
| AgileFormer |
- Avg DSC
- 86.11
- Avg HD
- 12.88
|
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation 2024-03-29 |
코드 1 |
| MERIT |
- Avg DSC
- 84.90
- Avg HD
- 13.22
|
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation 2023-03-29 |
코드 1 |
| Automatic AI-SAM |
- Avg DSC
- 84.21
|
AI-SAM: Automatic and Interactive Segment Anything Model 2023-12-05 |
코드 1 |
| ParaTransCNN |
- Avg DSC
- 83.86
- Avg HD
- 15.86
|
ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation 2024-01-27 |
코드 1 |
| EMCAD |
- Avg DSC
- 83.63
- Avg HD
- 15.68
|
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation 2024-05-11 |
코드 1 |
| PAG-TransYnet |
- Avg DSC
- 83.43
- Avg HD
- 15.82
|
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging 2024-04-28 |
코드 1 |
| SegFormer3D |
- Avg DSC
- 82.15
|
SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation 2024-04-15 |
코드 1 · 코드 2 |
| MISSFormer |
- Avg DSC
- 81.96
- Avg HD
- 18.20
|
MISSFormer: An Effective Medical Image Segmentation Transformer 2021-09-15 |
코드 1 |
| TransUNet |
- Avg DSC
- 81.19
|
S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging 2024-12-17 |
코드 1 |
| SelfReg-UNet: SwinUNet |
- Avg DSC
- 80.54
|
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation 2024-06-21 |
코드 1 |
| SelfReg-UNet: Vanilla UNet |
- Avg DSC
- 80.34
|
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation 2024-06-21 |
코드 1 |
| FCB Former |
- Avg DSC
- 80.26
|
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation 2022-07-16 |
코드 1 |
| SETR |
- Avg DSC
- 79.60
|
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers 2020-12-31 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 |
| SwinUnet |
- Avg DSC
- 79.13
- Avg HD
- 21.55
|
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation 2021-05-12 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 |
| UCTransNet |
- Avg DSC
- 78.99
- Avg HD
- 30.29
|
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer 2021-09-09 |
코드 1 · 코드 2 · 코드 3 |
| TransUNet |
- Avg DSC
- 77.48
- Avg HD
- 31.69
|
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation 2021-02-08 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18 · 코드 19 · 코드 20 · 코드 21 · 코드 22 |