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Medical Image Segmentation On 2018 Data
Metriken
Dice
Precision
Recall
mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | Repository | |||||
|---|---|---|---|---|---|---|
| EMCAD | 0.9274 | - | - | - | EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation | |
| DuAT | 0.926 | - | - | 0.870 | DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation | |
| SSFormer-L | 0.9230 | - | - | 0.8614 | Stepwise Feature Fusion: Local Guides Global | |
| Trans2Unet | 0.9225 | - | - | 0.8614 | Trans2Unet: Neural fusion for Nuclei Semantic Segmentation | - |
| MSRF-Net | 0.9224 | 0.9022 | 0.9402 | 0.8534 | MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation | |
| FANet | 0.9176 | 0.9194 | 0.9222 | 0.8569 | FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation | |
| DoubleUNet | 0.9133 | 0.9596 | 0.6407 | 0.8407 | DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation | |
| Unet++ | 0.8974 | - | - | 0.9255 | UNet++: A Nested U-Net Architecture for Medical Image Segmentation | |
| DCSAU-Net | - | - | 0.9240 | 0.8501 | DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation |
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