task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mIoU | 0.748 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mean Dice | 0.823 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mIoU | 0.746 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | DuAT | https://arxiv.org/abs/2212.11677v1 | Average MAE | 0.013 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mean Dice | 0.822 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mIoU | 0.7258 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mean Dice | 0.8007 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mIoU | 0.720 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mean Dice | 0.796 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.709 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.789 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | mIoU | 0.704 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | mean Dice | 0.78 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.689 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.012 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.766 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.859 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.905 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mIoU | 0.681 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mean Dice | 0.757 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mIoU | 0.672 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | Average MAE | 0.017 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mean Dice | 0.747 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | S-Measure | 0.868 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | max E-Measure | 0.894 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.665 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.739 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.661 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.737 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.659 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.733 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | HarDNet-DFUS | https://arxiv.org/abs/2209.07313v1 | mean Dice | 0.730 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.648 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.015 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.711 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.844 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.887 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.615 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.023 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.694 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.815 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.851 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mIoU | 0.613 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mean Dice | 0.677 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mIoU | 0.7534 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mean Dice | 0.6364 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PraNet | https://arxiv.org/abs/2006.11392v4 | mIoU | 0.5670 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PraNet | https://arxiv.org/abs/2006.11392v4 | Average MAE | 0.031 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PraNet | https://arxiv.org/abs/2006.11392v4 | mean Dice | 0.6280 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PraNet | https://arxiv.org/abs/2006.11392v4 | S-Measure | 0.794 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | PraNet | https://arxiv.org/abs/2006.11392v4 | max E-Measure | 0.841 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.7458 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mean Dice | 0.6136 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | NanoNet-A | https://arxiv.org/abs/2104.11138v1 | DSC | 0.7364 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | NanoNet-A | https://arxiv.org/abs/2104.11138v1 | mIoU | 0.6319 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | NanoNet-A | https://arxiv.org/abs/2104.11138v1 | Recall | 0.8566 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | NanoNet-A | https://arxiv.org/abs/2104.11138v1 | Precision | 0.7310 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | NanoNet-A | https://arxiv.org/abs/2104.11138v1 | FPS | 28.07 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | https://arxiv.org/abs/2012.15247v1 | DSC | 0.8154 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | https://arxiv.org/abs/2012.15247v1 | mIoU | 0.7396 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | https://arxiv.org/abs/2012.15247v1 | Recall | 0.8533 |
Medical Image Segmentation | Medico automatic polyp segmentation challenge (dataset) | UNet-ResNet50 | https://arxiv.org/abs/2012.15247v1 | Precision | 0.8533 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | Dice | 0.8125 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.8467 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | Recall | 0.6896 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | precision | 0.6421 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | Dice | 0.8130 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.8477 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | Recall | 0.6875 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | precision | 0.6276 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | Dice | 0.8798 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mIoU | 0.8730 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | Recall | 0.7749 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | precision | 0.6702 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + CRF | https://arxiv.org/abs/2107.12435v1 | Dice | 0.8811 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + CRF | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.8739 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + CRF | https://arxiv.org/abs/2107.12435v1 | Recall | 0.7743 |
Medical Image Segmentation | CVC-VideoClinicDB | ResUNet++ + CRF | https://arxiv.org/abs/2107.12435v1 | precision | 0.6706 |
Medical Image Segmentation | CVC-VideoClinicDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | Dice | 0.926 |
Medical Image Segmentation | CVC-VideoClinicDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | mIoU | 0.862 |
Medical Image Segmentation | Autoimmune Dataset | Unet with APP | https://arxiv.org/abs/2207.06489v5 | IoU | 0.4983 |
Medical Image Segmentation | Kvasir-Instrument | efficientnetb1 | https://journals.uio.no/NMI/article/view/9132 | DSC | 0.948 |
Medical Image Segmentation | Kvasir-Instrument | efficientnetb1 | https://journals.uio.no/NMI/article/view/9132 | Dice Score | 0.948 |
Medical Image Segmentation | Kvasir-Instrument | efficientnetb1 | https://journals.uio.no/NMI/article/view/9132 | Intersection over Union | 0.911 |
Medical Image Segmentation | Kvasir-Instrument | UNet | https://arxiv.org/abs/2011.08065v1 | DSC | 0.9158 |
Medical Image Segmentation | Kvasir-Instrument | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | DSC | 0.9038 |
Medical Image Segmentation | MICCAI 2015 Head and Neck Challenge | AnatomyNet | http://arxiv.org/abs/1808.05238v2 | Dice | 79.25 |
Medical Image Segmentation | CVC-ColonDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mean Dice | 0.9526 |
Medical Image Segmentation | CVC-ColonDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mIoU | 0.9096 |
Medical Image Segmentation | CVC-ColonDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mean Dice | 0.9353 |
Medical Image Segmentation | CVC-ColonDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mIoU | 0.8785 |
Medical Image Segmentation | CVC-ColonDB | EMCAD | https://arxiv.org/abs/2405.06880v1 | mean Dice | 0.9231 |
Medical Image Segmentation | CVC-ColonDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mean Dice | 0.921 |
Medical Image Segmentation | CVC-ColonDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mIoU | 0.854 |
Medical Image Segmentation | CVC-ColonDB | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mean Dice | 0.919 |
Medical Image Segmentation | CVC-ColonDB | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mIoU | 0.856 |
Medical Image Segmentation | CVC-ColonDB | ProMISe | https://arxiv.org/abs/2403.04164v3 | mean Dice | 0.874 |
Medical Image Segmentation | CVC-ColonDB | ProMISe | https://arxiv.org/abs/2403.04164v3 | mIoU | 0.789 |
Medical Image Segmentation | CVC-ColonDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | mean Dice | 0.867 |
Medical Image Segmentation | CVC-ColonDB | Meta-Polyp | https://arxiv.org/abs/2305.07848v3 | mIoU | 0.79 |
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