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 | CVC-ColonDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mean Dice | 0.8474 |
Medical Image Segmentation | CVC-ColonDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.8466 |
Medical Image Segmentation | CVC-ColonDB | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mean Dice | 0.8261 |
Medical Image Segmentation | CVC-ColonDB | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.7460 |
Medical Image Segmentation | CVC-ColonDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mean Dice | 0.8254 |
Medical Image Segmentation | CVC-ColonDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mIoU | 0.7453 |
Medical Image Segmentation | CVC-ColonDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mean Dice | 0.819 |
Medical Image Segmentation | CVC-ColonDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mIoU | 0.737 |
Medical Image Segmentation | CVC-ColonDB | DuAT | https://arxiv.org/abs/2212.11677v1 | Average MAE | 0.026 |
Medical Image Segmentation | CVC-ColonDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mean Dice | 0.811 |
Medical Image Segmentation | CVC-ColonDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mIoU | 0.730 |
Medical Image Segmentation | CVC-ColonDB | Polyp-PVT | https://arxiv.org/abs/2108.06932v8 | mean Dice | 0.808 |
Medical Image Segmentation | CVC-ColonDB | Polyp-PVT | https://arxiv.org/abs/2108.06932v8 | mIoU | 0.727 |
Medical Image Segmentation | CVC-ColonDB | Polyp-PVT | https://arxiv.org/abs/2108.06932v8 | Average MAE | 0.031 |
Medical Image Segmentation | CVC-ColonDB | Polyp-PVT | https://arxiv.org/abs/2108.06932v8 | S-Measure | 0.865 |
Medical Image Segmentation | CVC-ColonDB | Polyp-PVT | https://arxiv.org/abs/2108.06932v8 | max E-Measure | 0.913 |
Medical Image Segmentation | CVC-ColonDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mean Dice | 0.802 |
Medical Image Segmentation | CVC-ColonDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mIoU | 0.721 |
Medical Image Segmentation | CVC-ColonDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.783 |
Medical Image Segmentation | CVC-ColonDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.704 |
Medical Image Segmentation | CVC-ColonDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.034 |
Medical Image Segmentation | CVC-ColonDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.848 |
Medical Image Segmentation | CVC-ColonDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.897 |
Medical Image Segmentation | CVC-ColonDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mean Dice | 0.774 |
Medical Image Segmentation | CVC-ColonDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mIoU | 0.689 |
Medical Image Segmentation | CVC-ColonDB | HarDNet-DFUS | https://arxiv.org/abs/2209.07313v1 | mean Dice | 0.774 |
Medical Image Segmentation | CVC-ColonDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mean Dice | 0.773 |
Medical Image Segmentation | CVC-ColonDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mIoU | 0.689 |
Medical Image Segmentation | CVC-ColonDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | Average MAE | 0.042 |
Medical Image Segmentation | CVC-ColonDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | S-Measure | 0.853 |
Medical Image Segmentation | CVC-ColonDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | max E-Measure | 0.902 |
Medical Image Segmentation | CVC-ColonDB | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.773 |
Medical Image Segmentation | CVC-ColonDB | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.696 |
Medical Image Segmentation | CVC-ColonDB | KDAS | https://arxiv.org/abs/2312.08555v3 | mean Dice | 0.759 |
Medical Image Segmentation | CVC-ColonDB | KDAS | https://arxiv.org/abs/2312.08555v3 | mIoU | 0.679 |
Medical Image Segmentation | CVC-ColonDB | KDAS | https://arxiv.org/abs/2312.08555v3 | Average MAE | 0.032 |
Medical Image Segmentation | CVC-ColonDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.754 |
Medical Image Segmentation | CVC-ColonDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.689 |
Medical Image Segmentation | CVC-ColonDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.037 |
Medical Image Segmentation | CVC-ColonDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.849 |
Medical Image Segmentation | CVC-ColonDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.897 |
Medical Image Segmentation | CVC-ColonDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.751 |
Medical Image Segmentation | CVC-ColonDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.678 |
Medical Image Segmentation | CVC-ColonDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.039 |
Medical Image Segmentation | CVC-ColonDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.835 |
Medical Image Segmentation | CVC-ColonDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.878 |
Medical Image Segmentation | CVC-ColonDB | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.744 |
Medical Image Segmentation | CVC-ColonDB | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.676 |
Medical Image Segmentation | CVC-ColonDB | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mean Dice | 0.731 |
Medical Image Segmentation | CVC-ColonDB | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mIoU | 0.660 |
Medical Image Segmentation | CVC-ColonDB | PraNet | https://arxiv.org/abs/2006.11392v4 | mean Dice | 0.709 |
Medical Image Segmentation | CVC-ColonDB | PraNet | https://arxiv.org/abs/2006.11392v4 | mIoU | 0.649 |
Medical Image Segmentation | CVC-ColonDB | PraNet | https://arxiv.org/abs/2006.11392v4 | Average MAE | 0.045 |
Medical Image Segmentation | CVC-ColonDB | PraNet | https://arxiv.org/abs/2006.11392v4 | S-Measure | 0.819 |
Medical Image Segmentation | CVC-ColonDB | PraNet | https://arxiv.org/abs/2006.11392v4 | max E-Measure | 0.869 |
Medical Image Segmentation | PROMISE12 | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | F1 | 90.79 |
Medical Image Segmentation | Medical Segmentation Decathlon | Swin UNETR | https://arxiv.org/abs/2111.14791v2 | Dice (Average) | 78.68 |
Medical Image Segmentation | Medical Segmentation Decathlon | Swin UNETR | https://arxiv.org/abs/2111.14791v2 | NSD | 89.28 |
Medical Image Segmentation | Medical Segmentation Decathlon | DiNTS | https://arxiv.org/abs/2103.15954v1 | Dice (Average) | 77.93 |
Medical Image Segmentation | Medical Segmentation Decathlon | DiNTS | https://arxiv.org/abs/2103.15954v1 | NSD | 88.68 |
Medical Image Segmentation | Medical Segmentation Decathlon | nnUNet | http://arxiv.org/abs/1809.10486v1 | Dice (Average) | 77.89 |
Medical Image Segmentation | Medical Segmentation Decathlon | nnUNet | http://arxiv.org/abs/1809.10486v1 | NSD | 88.09 |
Medical Image Segmentation | Medical Segmentation Decathlon | Models Genesis | https://arxiv.org/abs/1908.06912v1 | Dice (Average) | 76.97 |
Medical Image Segmentation | Medical Segmentation Decathlon | Models Genesis | https://arxiv.org/abs/1908.06912v1 | NSD | 87.19 |
Medical Image Segmentation | Medical Segmentation Decathlon | Trans VW | https://arxiv.org/abs/2102.10680v1 | Dice (Average) | 76.96 |
Medical Image Segmentation | Medical Segmentation Decathlon | Trans VW | https://arxiv.org/abs/2102.10680v1 | NSD | 87.64 |
Medical Image Segmentation | Endotect Polyp Segmentation Challenge Dataset | DDANet | https://arxiv.org/abs/2012.15245v1 | DSC | 0.7870 |
Medical Image Segmentation | Endotect Polyp Segmentation Challenge Dataset | DDANet | https://arxiv.org/abs/2012.15245v1 | mIoU | 0.701 |
Medical Image Segmentation | Endotect Polyp Segmentation Challenge Dataset | DDANet | https://arxiv.org/abs/2012.15245v1 | FPS | 70.23 |
Medical Image Segmentation | Endotect Polyp Segmentation Challenge Dataset | PEFNet | https://arxiv.org/abs/2301.06673v2 | DSC | 0.8565 |
Medical Image Segmentation | Endotect Polyp Segmentation Challenge Dataset | PEFNet | https://arxiv.org/abs/2301.06673v2 | mIoU | 0.7967 |
Medical Image Segmentation | MoNuSeg | Stardist | http://arxiv.org/abs/1806.03535v2 | F1 | 84.6 |
Medical Image Segmentation | MoNuSeg | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | F1 | 84.12 |
Medical Image Segmentation | MoNuSeg | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | IoU | 73.06 |
Medical Image Segmentation | MoNuSeg | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | AHD95 | 2.2422 |
Medical Image Segmentation | MoNuSeg | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | ASD | 0.1583 |
Medical Image Segmentation | MoNuSeg | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | F1 | 82.50 |
Medical Image Segmentation | MoNuSeg | LViT-L | https://arxiv.org/abs/2206.14718v4 | F1 | 81.01 |
Medical Image Segmentation | MoNuSeg | LViT-L | https://arxiv.org/abs/2206.14718v4 | IoU | 68.2 |
Medical Image Segmentation | MoNuSeg | MDM | https://arxiv.org/abs/2308.05695v4 | F1 | 81.01 |
Medical Image Segmentation | MoNuSeg | LViT-LW | https://arxiv.org/abs/2206.14718v4 | F1 | 80.66 |
Medical Image Segmentation | MoNuSeg | LViT-LW | https://arxiv.org/abs/2206.14718v4 | IoU | 67.71 |
Medical Image Segmentation | MoNuSeg | UCTransNet | https://arxiv.org/abs/2206.14718v4 | F1 | 79.87 |
Medical Image Segmentation | MoNuSeg | UCTransNet | https://arxiv.org/abs/2206.14718v4 | IoU | 66.68 |
Medical Image Segmentation | MoNuSeg | LoGo | https://arxiv.org/abs/2102.10662v2 | F1 | 79.56 |
Medical Image Segmentation | MoNuSeg | LoGo | https://arxiv.org/abs/2102.10662v2 | IoU | 66.17 |
Medical Image Segmentation | MoNuSeg | MedT | https://arxiv.org/abs/2102.10662v2 | F1 | 79.55 |
Medical Image Segmentation | MoNuSeg | MedT | https://arxiv.org/abs/2102.10662v2 | IoU | 66.17 |
Medical Image Segmentation | MoNuSeg | GTUNet | https://arxiv.org/abs/2206.14718v4 | F1 | 79.26 |
Medical Image Segmentation | MoNuSeg | GTUNet | https://arxiv.org/abs/2206.14718v4 | IoU | 65.94 |
Medical Image Segmentation | MoNuSeg | UNet++ | https://arxiv.org/abs/2206.14718v4 | F1 | 77.01 |
Medical Image Segmentation | MoNuSeg | UNet++ | https://arxiv.org/abs/2206.14718v4 | IoU | 63.04 |
Medical Image Segmentation | MoNuSeg | U-Net | https://arxiv.org/abs/2102.10662v2 | F1 | 76.83 |
Medical Image Segmentation | MoNuSeg | U-Net | https://arxiv.org/abs/2102.10662v2 | IoU | 62.49 |
Medical Image Segmentation | MoNuSeg | HistoSeg | https://arxiv.org/abs/2209.00729v1 | IoU | 71.06 |
Medical Image Segmentation | MoNuSeg | DoubleUnet-DCA | https://arxiv.org/abs/2303.17696v1 | IoU | 65.97 |
Medical Image Segmentation | MoNuSeg | MCADS-Decoder | https://arxiv.org/abs/2506.18335v1 | mIoU | 74.04 |
Medical Image Segmentation | MoNuSeg 2018 | MaxViT-UNet | https://arxiv.org/abs/2305.08396v5 | Dice | 0.8378 |
Medical Image Segmentation | MoNuSeg 2018 | MaxViT-UNet | https://arxiv.org/abs/2305.08396v5 | IoU | 0.7208 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | RaBiT | https://arxiv.org/abs/2307.06420v1 | Average Dice | 0.94 |
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