Medical Image Segmentation On Drive 1
Metrics
F1 score
Precision
Recall
Specificity
mIoU
Results
Performance results of various models on this benchmark
Model Name | F1 score | Precision | Recall | Specificity | mIoU | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| FANet | 0.8183 | 0.8189 | 0.8215 | 0.9826 | 0.6927 | FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation | |
| MERIT-GCASCADE | 0.8290 | - | 0.8281 | 0.9844 | 0.7081 | G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | |
| Hi-gMISnet | - | - | - | - | 0.6901 | Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN | |
| BCDU-net | 0.8222 | - | - | - | - | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | |
| PVT-GCASCADE | 0.8210 | - | 0.83 | 0.9822 | 0.697 | G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation |
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