Polyp Segmentation On Kvasir Seg
评估指标
mDice
mIoU
评测结果
各个模型在此基准测试上的表现结果
模型名称 | mDice | mIoU | Paper Title | Repository |
|---|---|---|---|---|
| KDAS | 0.913 | 0.848 | KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation | |
| ResUNet++ | 0.8133 | 0.7927 | ResUNet++: An Advanced Architecture for Medical Image Segmentation | - |
| SSFormer-S + PRN | - | 0.891 | PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization | |
| PEFNet | 0.8818 | 0.8163 | Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation | |
| ResUNet | 0.7877 | - | Kvasir-SEG: A Segmented Polyp Dataset | - |
| TransNetR | 0.8706 | 0.8016 | TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing | |
| TGA-Net | 0.8982 | 0.8330 | TGANet: Text-guided attention for improved polyp segmentation | |
| PVT-CASCADE | 0.9258 | 0.8776 | Medical Image Segmentation via Cascaded Attention Decoding |
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