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 | BKAI-IGH NeoPolyp-Small | RaBiT | https://arxiv.org/abs/2307.06420v1 | mIoU | 0.886 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | EMCAD | https://arxiv.org/abs/2405.06880v1 | Average Dice | 0.9296 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | TransResU-Net | https://arxiv.org/abs/2206.08985v1 | Average Dice | 0.9154 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | TransResU-Net | https://arxiv.org/abs/2206.08985v1 | mIoU | 0.8568 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | TGANet | https://arxiv.org/abs/2205.04280v1 | Average Dice | 0.9023 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | TGANet | https://arxiv.org/abs/2205.04280v1 | mIoU | 0.8409 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | NeoUNet | https://arxiv.org/abs/2107.05023v1 | Average Dice | 0.80723 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | FocalUNet | https://arxiv.org/abs/2212.09263v1 | Average Dice | 0.8021 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | BlazeNeo | https://arxiv.org/abs/2203.00129v1 | Average Dice | 0.78802 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | ColonSegNet | https://arxiv.org/abs/2011.07631v2 | Average Dice | 0.6881 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | QTSeg | https://arxiv.org/abs/2412.17241v1 | MAE (5-folds) | 0.06 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | QTSeg | https://arxiv.org/abs/2412.17241v1 | Average Dice (5-folds) | 93.13 |
Medical Image Segmentation | BKAI-IGH NeoPolyp-Small | QTSeg | https://arxiv.org/abs/2412.17241v1 | mIoU (5-folds) | 88.94 |
Medical Image Segmentation | ASU-Mayo Clinic dataset | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mIoU | 0.8569 |
Medical Image Segmentation | ASU-Mayo Clinic dataset | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | Recall | 0.6534 |
Medical Image Segmentation | ASU-Mayo Clinic dataset | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | Precision | 0.4896 |
Medical Image Segmentation | ASU-Mayo Clinic dataset | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | DSC | 0.8743 |
Medical Image Segmentation | HSVM | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | Dice Score | 83.2 |
Medical Image Segmentation | HSVM | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | MSD | 1.19 |
Medical Image Segmentation | HSVM | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | VS | 94.45 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | MERIT | https://arxiv.org/abs/2303.16892v1 | Avg DSC | 84.90 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | MERIT | https://arxiv.org/abs/2303.16892v1 | Avg HD | 13.22 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg DSC | 84.54 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg HD | 10.38 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | RWKV-UNet | https://arxiv.org/abs/2501.08458v1 | Avg DSC | 84.02 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | RWKV-UNet | https://arxiv.org/abs/2501.08458v1 | Avg HD | 15.7 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | EMCAD | https://arxiv.org/abs/2405.06880v1 | Avg DSC | 83.63 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | EMCAD | https://arxiv.org/abs/2405.06880v1 | Avg HD | 15.68 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg DSC | 83.28 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg HD | 15.83 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | TransCASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg DSC | 82.68 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | TransCASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg HD | 17.34 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | 3D Att-UNet-MTL-TSOL | https://arxiv.org/abs/2210.04285v1 | Avg DSC | 82 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg DSC | 81.06 |
Medical Image Segmentation | MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg HD | 20.23 |
Medical Image Segmentation | EM | EMCAD | https://arxiv.org/abs/2405.06880v1 | DSC | 95.53 |
Medical Image Segmentation | EM | FANet | https://arxiv.org/abs/2103.17235v3 | IoU | 0.9134 |
Medical Image Segmentation | EM | FANet | https://arxiv.org/abs/2103.17235v3 | DSC | 0.9547 |
Medical Image Segmentation | EM | FANet | https://arxiv.org/abs/2103.17235v3 | Recall | 0.9568 |
Medical Image Segmentation | EM | FANet | https://arxiv.org/abs/2103.17235v3 | Specificity | 0.8096 |
Medical Image Segmentation | EM | FANet | https://arxiv.org/abs/2103.17235v3 | Precision | 0.9529 |
Medical Image Segmentation | EM | UNet++ | https://arxiv.org/abs/1912.05074v2 | IoU | 89.33 |
Medical Image Segmentation | Cell | UNet++ | https://arxiv.org/abs/1912.05074v2 | IoU | 91.21 |
Medical Image Segmentation | ROBUST-MIS | ColonsegNet | https://arxiv.org/abs/2107.02319v2 | DSC | 0.8495 |
Medical Image Segmentation | ROBUST-MIS | ColonsegNet | https://arxiv.org/abs/2107.02319v2 | mIoU | 0.7943 |
Medical Image Segmentation | ROBUST-MIS | ColonsegNet | https://arxiv.org/abs/2107.02319v2 | FPS | 185.54 |
Medical Image Segmentation | ROBUST-MIS | DDANet | https://arxiv.org/abs/2107.02319v2 | DSC | 0.8739 |
Medical Image Segmentation | ROBUST-MIS | DDANet | https://arxiv.org/abs/2107.02319v2 | mIoU | 0.8183 |
Medical Image Segmentation | ROBUST-MIS | DDANet | https://arxiv.org/abs/2107.02319v2 | FPS | 101.36 |
Medical Image Segmentation | ROBUST-MIS | AdapterSIS | https://doi.org/10.1007/s11548-024-03140-z | DSC | 92.9 |
Medical Image Segmentation | ROBUST-MIS | AdapterSIS | https://doi.org/10.1007/s11548-024-03140-z | mIoU | 86.6 |
Medical Image Segmentation | ROBUST-MIS | SegMatch | https://arxiv.org/abs/2308.05232v1 | DSC | 93.7 |
Medical Image Segmentation | MosMedData | C2FVL | https://arxiv.org/abs/2303.00279v1 | Average Dice | 74.56 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | FCT | https://arxiv.org/abs/2207.07842v1 | Avg DSC | 94.26 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | Interactive AI-SAM gt box | https://arxiv.org/abs/2312.03119v1 | Avg DSC | 93.89 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | FCT | https://arxiv.org/abs/2206.00566v2 | Avg DSC | 93.02 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | LHU-Net | https://arxiv.org/abs/2404.05102v2 | Avg DSC | 92.65 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | MIST | https://arxiv.org/abs/2310.19898v1 | Avg DSC | 92.56 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | MERIT | https://arxiv.org/abs/2303.16892v1 | Avg DSC | 92.32 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg DSC | 92.23 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | EMCAD | https://arxiv.org/abs/2405.06880v1 | Avg DSC | 92.12 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | nnFormer | https://arxiv.org/abs/2109.03201v6 | Avg DSC | 92.06 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | Automatic AI-SAM | https://arxiv.org/abs/2312.03119v1 | Avg DSC | 92.06 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Avg DSC | 91.95 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | TransCASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg DSC | 91.63 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | Avg DSC | 91.46 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | SegFormer3D | https://arxiv.org/abs/2404.10156v2 | Avg DSC | 90.96 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | TransUNet | https://arxiv.org/abs/2412.13156v1 | Avg DSC | 90.4 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | SwinUnet | https://arxiv.org/abs/2105.05537v1 | Avg DSC | 90.00 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | TransUNet | https://arxiv.org/abs/2102.04306v1 | Avg DSC | 89.71 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | MISSFormer | https://arxiv.org/abs/2109.07162v1 | Avg DSC | 87.9 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | R50-ViT-CUP | https://arxiv.org/abs/2102.04306v1 | Avg DSC | 87.57 |
Medical Image Segmentation | Automatic Cardiac Diagnosis Challenge (ACDC) | R50-AttnUNet | https://arxiv.org/abs/2102.04306v1 | Avg DSC | 86.75 |
Medical Image Segmentation | KvasirCapsule-SEG | NanoNet | https://arxiv.org/abs/2104.11138v1 | DSC | 0.9493 |
Medical Image Segmentation | KvasirCapsule-SEG | NanoNet | https://arxiv.org/abs/2104.11138v1 | mIoU | 0.9059 |
Medical Image Segmentation | KvasirCapsule-SEG | ResUNet+ | https://arxiv.org/abs/1911.07067v1 | DSC | 0.9499 |
Medical Image Segmentation | KvasirCapsule-SEG | ResUNet+ | https://arxiv.org/abs/1911.07067v1 | mIoU | 0.9087 |
Medical Image Segmentation | LiTS2017 | UNet 3+ | https://arxiv.org/abs/2004.08790v1 | Dice | 0.9675 |
Medical Image Segmentation | LiTS2017 | UNet 3+ w/o DS | https://arxiv.org/abs/2004.08790v1 | Dice | 0.9580 |
Medical Image Segmentation | Hyper-Kvasir Dataset | efficientnetb1 | https://journals.uio.no/NMI/article/view/9132 | Dice score | 0.857 |
Medical Image Segmentation | Hyper-Kvasir Dataset | efficientnetb1 | https://journals.uio.no/NMI/article/view/9132 | Intersection over Union | 0.800 |
Medical Image Segmentation | ISBI 2012 EM Segmentation | DC-UNet | https://arxiv.org/abs/2006.00414v1 | Jaccard | 0.9262 |
Medical Image Segmentation | ISBI 2012 EM Segmentation | CE-Net | http://arxiv.org/abs/1903.02740v1 | VInfo | 0.9878 |
Medical Image Segmentation | ISBI 2012 EM Segmentation | CE-Net | http://arxiv.org/abs/1903.02740v1 | VRand | 0.9743 |
Medical Image Segmentation | ISBI 2012 EM Segmentation | U-Net | http://arxiv.org/abs/1505.04597v1 | Warping Error | 0.000353 |
Medical Image Segmentation | ISIC 2018 | EMCAD | https://arxiv.org/abs/2405.06880v1 | DSC | 90.96 |
Medical Image Segmentation | ISIC 2018 | DCSAU-Net | https://arxiv.org/abs/2202.00972v2 | DSC | 90.35 |
Medical Image Segmentation | ACDC | FCT | https://arxiv.org/abs/2206.00566v2 | Dice Score | 0.9302 |
Medical Image Segmentation | ACDC | AgileFormer | https://arxiv.org/abs/2404.00122v2 | Dice Score | 0.9255 |
Medical Image Segmentation | ACDC | RWKV-UNet | https://arxiv.org/abs/2501.08458v1 | Dice Score | 0.9217 |
Medical Image Segmentation | ACDC | EMCAD | https://arxiv.org/abs/2405.06880v1 | Dice Score | 0.9212 |
Medical Image Segmentation | ACDC | Swin UNet | https://arxiv.org/abs/2105.05537v1 | Dice Score | 0.9 |
Medical Image Segmentation | ACDC | TransUNet | https://arxiv.org/abs/2102.04306v1 | Dice Score | 0.8971 |
Medical Image Segmentation | AMOS | MedNeXt-L (5x5x5) | https://arxiv.org/abs/2303.09975v5 | Average Dice | 91.77 |
Medical Image Segmentation | 2018 Data Science Bowl | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | Dice | 92.79 |
Medical Image Segmentation | 2018 Data Science Bowl | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | mIoU | 87.22 |
Medical Image Segmentation | 2018 Data Science Bowl | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | AHD95 | 6.5914 |
Medical Image Segmentation | 2018 Data Science Bowl | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | ASD | 1.7074 |
Medical Image Segmentation | 2018 Data Science Bowl | EMCAD | https://arxiv.org/abs/2405.06880v1 | Dice | 0.9274 |
Medical Image Segmentation | 2018 Data Science Bowl | DuAT | https://arxiv.org/abs/2212.11677v1 | Dice | 0.926 |
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