Medical Image Segmentation On Cvc
Metrics
Dice
Recall
mIoU
precision
Results
Performance results of various models on this benchmark
Model Name | Dice | Recall | mIoU | precision | Paper Title | Repository |
|---|---|---|---|---|---|---|
| ResUNet++ + TTA + CRF | 0.8130 | 0.6875 | 0.8477 | 0.6276 | A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation | |
| ResUNet++ + TTA | 0.8125 | 0.6896 | 0.8467 | 0.6421 | A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation | |
| Meta-Polyp | 0.926 | - | 0.862 | - | Meta-Polyp: a baseline for efficient Polyp segmentation | |
| ResUNet++ + CRF | 0.8811 | 0.7743 | 0.8739 | 0.6706 | A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation | |
| ResUNet++ | 0.8798 | 0.7749 | 0.8730 | 0.6702 | ResUNet++: An Advanced Architecture for Medical Image Segmentation | - |
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