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 | Kvasir-SEG | ColonSegNet | https://arxiv.org/abs/2011.07631v2 | mIoU | 0.7239 |
Medical Image Segmentation | Kvasir-SEG | ColonSegNet | https://arxiv.org/abs/2011.07631v2 | FPS | 182.38 |
Medical Image Segmentation | Kvasir-SEG | U-Net | http://arxiv.org/abs/1505.04597v1 | Average MAE | 0.055 |
Medical Image Segmentation | Kvasir-SEG | U-Net | http://arxiv.org/abs/1505.04597v1 | mean Dice | 0.8180 |
Medical Image Segmentation | Kvasir-SEG | U-Net | http://arxiv.org/abs/1505.04597v1 | S-Measure | 0.858 |
Medical Image Segmentation | Kvasir-SEG | U-Net | http://arxiv.org/abs/1505.04597v1 | max E-Measure | 0.893 |
Medical Image Segmentation | Kvasir-SEG | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mean Dice | 0.8133 |
Medical Image Segmentation | Kvasir-SEG | ResUNet | https://arxiv.org/abs/1911.07069v1 | mean Dice | 0.7877 |
Medical Image Segmentation | Kvasir-SEG | RUPNet | https://arxiv.org/abs/2301.02703v2 | mean Dice | 0.7658 |
Medical Image Segmentation | Kvasir-SEG | RUPNet | https://arxiv.org/abs/2301.02703v2 | mIoU | 0.6553 |
Medical Image Segmentation | Kvasir-SEG | RUPNet | https://arxiv.org/abs/2301.02703v2 | FPS | 152.60 |
Medical Image Segmentation | 2015 MICCAI Polyp Detection | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | Dice | 0.7649 |
Medical Image Segmentation | ISIC 2018 | ProMISe | https://arxiv.org/abs/2403.04164v3 | DSC | 92.10 |
Medical Image Segmentation | ISIC 2018 | ProMISe | https://arxiv.org/abs/2403.04164v3 | mIoU | 85.00 |
Medical Image Segmentation | ISIC 2018 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | DSC | 91.51 |
Medical Image Segmentation | ISIC 2018 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 86.53 |
Medical Image Segmentation | ISIC 2018 | EMCAD | https://arxiv.org/abs/2405.06880v1 | DSC | 90.96 |
Medical Image Segmentation | ISIC 2018 | UNeXt | https://arxiv.org/abs/2203.04967v1 | DSC | 89.70 |
Medical Image Segmentation | ISIC 2018 | FANet | https://arxiv.org/abs/2103.17235v3 | DSC | 87.31 |
Medical Image Segmentation | Synapse multi-organ CT | Interactive AI-SAM gt box | https://arxiv.org/abs/2312.03119v1 | Avg DSC | 90.66 |
Medical Image Segmentation | Synapse multi-organ CT | Medical SAM Adapter | https://arxiv.org/abs/2304.12620v7 | Avg DSC | 89.80 |
Medical Image Segmentation | Synapse multi-organ CT | MedSegDiff-v2 | https://arxiv.org/abs/2301.11798v2 | Avg DSC | 89.50 |
Medical Image Segmentation | Synapse multi-organ CT | nnUNet | http://arxiv.org/abs/1809.10486v1 | Avg DSC | 88.80 |
Medical Image Segmentation | Synapse multi-organ CT | nnUNet | http://arxiv.org/abs/1809.10486v1 | Avg HD | 10.78 |
Medical Image Segmentation | Synapse multi-organ CT | MedNeXt-L (5x5x5) | https://arxiv.org/abs/2303.09975v5 | Avg DSC | 88.76 |
Medical Image Segmentation | Synapse multi-organ CT | MIST | https://arxiv.org/abs/2310.19898v1 | Avg DSC | 86.92 |
Medical Image Segmentation | Synapse multi-organ CT | MIST | https://arxiv.org/abs/2310.19898v1 | Avg HD | 11.07 |
Medical Image Segmentation | Synapse multi-organ CT | nnFormer | https://arxiv.org/abs/2109.03201v6 | Avg DSC | 86.57 |
Medical Image Segmentation | Synapse multi-organ CT | nnFormer | https://arxiv.org/abs/2109.03201v6 | Avg HD | 10.63 |
Medical Image Segmentation | Synapse multi-organ CT | AgileFormer | https://arxiv.org/abs/2404.00122v2 | Avg DSC | 86.11 |
Medical Image Segmentation | Synapse multi-organ CT | AgileFormer | https://arxiv.org/abs/2404.00122v2 | Avg HD | 12.88 |
Medical Image Segmentation | Synapse multi-organ CT | MERIT | https://arxiv.org/abs/2303.16892v1 | Avg DSC | 84.90 |
Medical Image Segmentation | Synapse multi-organ CT | MERIT | https://arxiv.org/abs/2303.16892v1 | Avg HD | 13.22 |
Medical Image Segmentation | Synapse multi-organ CT | Automatic AI-SAM | https://arxiv.org/abs/2312.03119v1 | Avg DSC | 84.21 |
Medical Image Segmentation | Synapse multi-organ CT | ParaTransCNN | https://arxiv.org/abs/2401.15307v1 | Avg DSC | 83.86 |
Medical Image Segmentation | Synapse multi-organ CT | ParaTransCNN | https://arxiv.org/abs/2401.15307v1 | Avg HD | 15.86 |
Medical Image Segmentation | Synapse multi-organ CT | EMCAD | https://arxiv.org/abs/2405.06880v1 | Avg DSC | 83.63 |
Medical Image Segmentation | Synapse multi-organ CT | EMCAD | https://arxiv.org/abs/2405.06880v1 | Avg HD | 15.68 |
Medical Image Segmentation | Synapse multi-organ CT | PAG-TransYnet | https://arxiv.org/abs/2404.18199v1 | Avg DSC | 83.43 |
Medical Image Segmentation | Synapse multi-organ CT | PAG-TransYnet | https://arxiv.org/abs/2404.18199v1 | Avg HD | 15.82 |
Medical Image Segmentation | Synapse multi-organ CT | SegFormer3D | https://arxiv.org/abs/2404.10156v2 | Avg DSC | 82.15 |
Medical Image Segmentation | Synapse multi-organ CT | MISSFormer | https://arxiv.org/abs/2109.07162v1 | Avg DSC | 81.96 |
Medical Image Segmentation | Synapse multi-organ CT | MISSFormer | https://arxiv.org/abs/2109.07162v1 | Avg HD | 18.20 |
Medical Image Segmentation | Synapse multi-organ CT | TransUNet | https://arxiv.org/abs/2412.13156v1 | Avg DSC | 81.19 |
Medical Image Segmentation | Synapse multi-organ CT | SelfReg-UNet: SwinUNet | https://arxiv.org/abs/2406.14896v1 | Avg DSC | 80.54 |
Medical Image Segmentation | Synapse multi-organ CT | SelfReg-UNet: Vanilla UNet | https://arxiv.org/abs/2406.14896v1 | Avg DSC | 80.34 |
Medical Image Segmentation | Synapse multi-organ CT | FCB Former | https://arxiv.org/abs/2207.07842v1 | Avg DSC | 80.26 |
Medical Image Segmentation | Synapse multi-organ CT | SETR | https://arxiv.org/abs/2012.15840v3 | Avg DSC | 79.60 |
Medical Image Segmentation | Synapse multi-organ CT | SwinUnet | https://arxiv.org/abs/2105.05537v1 | Avg DSC | 79.13 |
Medical Image Segmentation | Synapse multi-organ CT | SwinUnet | https://arxiv.org/abs/2105.05537v1 | Avg HD | 21.55 |
Medical Image Segmentation | Synapse multi-organ CT | UCTransNet | https://arxiv.org/abs/2109.04335v3 | Avg DSC | 78.99 |
Medical Image Segmentation | Synapse multi-organ CT | UCTransNet | https://arxiv.org/abs/2109.04335v3 | Avg HD | 30.29 |
Medical Image Segmentation | Synapse multi-organ CT | TransUNet | https://arxiv.org/abs/2102.04306v1 | Avg DSC | 77.48 |
Medical Image Segmentation | Synapse multi-organ CT | TransUNet | https://arxiv.org/abs/2102.04306v1 | Avg HD | 31.69 |
Medical Image Segmentation | Brain US | MedT | https://arxiv.org/abs/2102.10662v2 | F1 | 88.84 |
Medical Image Segmentation | Brain US | MedT | https://arxiv.org/abs/2102.10662v2 | IoU | 81.34 |
Medical Image Segmentation | Brain US | LoGo | https://arxiv.org/abs/2102.10662v2 | F1 | 88.54 |
Medical Image Segmentation | Brain US | LoGo | https://arxiv.org/abs/2102.10662v2 | IoU | 80.84 |
Medical Image Segmentation | Brain US | U-Net | https://arxiv.org/abs/2102.10662v2 | F1 | 87.92 |
Medical Image Segmentation | Brain US | U-Net | https://arxiv.org/abs/2102.10662v2 | IoU | 80.14 |
Medical Image Segmentation | TNBC | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | IoU | 66.13 |
Medical Image Segmentation | TNBC | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | Dice | 78.99 |
Medical Image Segmentation | TNBC | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | AHD95 | 10.355 |
Medical Image Segmentation | GlaS | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | F1 | 93.25 |
Medical Image Segmentation | GlaS | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | Dice | 93.25 |
Medical Image Segmentation | GlaS | MDM | https://arxiv.org/abs/2308.05695v4 | F1 | 91.95 |
Medical Image Segmentation | GlaS | MDM | https://arxiv.org/abs/2308.05695v4 | IoU | 85.13 |
Medical Image Segmentation | GlaS | MDM | https://arxiv.org/abs/2308.05695v4 | Dice | 91.95 |
Medical Image Segmentation | GlaS | UCTransNet | https://arxiv.org/abs/2109.04335v3 | F1 | 90.18 |
Medical Image Segmentation | GlaS | UCTransNet | https://arxiv.org/abs/2109.04335v3 | IoU | 82.96 |
Medical Image Segmentation | GlaS | UCTransNet | https://arxiv.org/abs/2109.04335v3 | Dice | 90.18 |
Medical Image Segmentation | GlaS | Trans2Unet | https://arxiv.org/abs/2407.17181v1 | F1 | 89.84 |
Medical Image Segmentation | GlaS | Trans2Unet | https://arxiv.org/abs/2407.17181v1 | IoU | 82.54 |
Medical Image Segmentation | GlaS | Trans2Unet | https://arxiv.org/abs/2407.17181v1 | Dice | 89.84 |
Medical Image Segmentation | GlaS | U-Net++ | https://arxiv.org/abs/2109.04335v3 | F1 | 87.56 |
Medical Image Segmentation | GlaS | U-Net++ | https://arxiv.org/abs/2109.04335v3 | IoU | 79.13 |
Medical Image Segmentation | GlaS | U-Net++ | https://arxiv.org/abs/2109.04335v3 | Dice | 87.56 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2109.04335v3 | F1 | 85.45 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2109.04335v3 | IoU | 74.78 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2109.04335v3 | Dice | 85.45 |
Medical Image Segmentation | GlaS | MedT | https://arxiv.org/abs/2102.10662v2 | F1 | 81.02 |
Medical Image Segmentation | GlaS | MedT | https://arxiv.org/abs/2102.10662v2 | IoU | 69.61 |
Medical Image Segmentation | GlaS | MedT | https://arxiv.org/abs/2102.10662v2 | Dice | 81.02 |
Medical Image Segmentation | GlaS | LoGo | https://arxiv.org/abs/2102.10662v2 | F1 | 79.68 |
Medical Image Segmentation | GlaS | LoGo | https://arxiv.org/abs/2102.10662v2 | IoU | 67.69 |
Medical Image Segmentation | GlaS | LoGo | https://arxiv.org/abs/2102.10662v2 | Dice | 79.68 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2102.10662v2 | F1 | 76.26 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2102.10662v2 | IoU | 63.03 |
Medical Image Segmentation | GlaS | U-Net | https://arxiv.org/abs/2102.10662v2 | Dice | 76.26 |
Medical Image Segmentation | GlaS | HistoSeg | https://arxiv.org/abs/2209.00729v1 | IoU | 76.73 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mIoU | 0.9179 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mean Dice | 0.9572 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mIoU | 0.879 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mean Dice | 0.936 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mIoU | 0.8788 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mean Dice | 0.9354 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | EMCAD | https://arxiv.org/abs/2405.06880v1 | mean Dice | 0.9229 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ProMISe | https://arxiv.org/abs/2403.04164v3 | mIoU | 0.750 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | ProMISe | https://arxiv.org/abs/2403.04164v3 | mean Dice | 0.840 |
Medical Image Segmentation | ETIS-LARIBPOLYPDB | RSAFormer | https://www.sciencedirect.com/science/article/abs/pii/S0010482524003524#preview-section-abstract | mean Dice | 0.835 |
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