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 | 2018 Data Science Bowl | DuAT | https://arxiv.org/abs/2212.11677v1 | mIoU | 0.870 |
Medical Image Segmentation | 2018 Data Science Bowl | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | Dice | 0.9230 |
Medical Image Segmentation | 2018 Data Science Bowl | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mIoU | 0.8614 |
Medical Image Segmentation | 2018 Data Science Bowl | Trans2Unet | https://arxiv.org/abs/2407.17181v1 | Dice | 0.9225 |
Medical Image Segmentation | 2018 Data Science Bowl | Trans2Unet | https://arxiv.org/abs/2407.17181v1 | mIoU | 0.8614 |
Medical Image Segmentation | 2018 Data Science Bowl | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | Dice | 0.9224 |
Medical Image Segmentation | 2018 Data Science Bowl | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | mIoU | 0.8534 |
Medical Image Segmentation | 2018 Data Science Bowl | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | Recall | 0.9402 |
Medical Image Segmentation | 2018 Data Science Bowl | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | Precision | 0.9022 |
Medical Image Segmentation | 2018 Data Science Bowl | FANet | https://arxiv.org/abs/2103.17235v3 | Dice | 0.9176 |
Medical Image Segmentation | 2018 Data Science Bowl | FANet | https://arxiv.org/abs/2103.17235v3 | mIoU | 0.8569 |
Medical Image Segmentation | 2018 Data Science Bowl | FANet | https://arxiv.org/abs/2103.17235v3 | Recall | 0.9222 |
Medical Image Segmentation | 2018 Data Science Bowl | FANet | https://arxiv.org/abs/2103.17235v3 | Precision | 0.9194 |
Medical Image Segmentation | 2018 Data Science Bowl | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | Dice | 0.9133 |
Medical Image Segmentation | 2018 Data Science Bowl | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | mIoU | 0.8407 |
Medical Image Segmentation | 2018 Data Science Bowl | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | Recall | 0.6407 |
Medical Image Segmentation | 2018 Data Science Bowl | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | Precision | 0.9596 |
Medical Image Segmentation | 2018 Data Science Bowl | Unet++ | http://arxiv.org/abs/1807.10165v1 | Dice | 0.8974 |
Medical Image Segmentation | 2018 Data Science Bowl | Unet++ | http://arxiv.org/abs/1807.10165v1 | mIoU | 0.9255 |
Medical Image Segmentation | 2018 Data Science Bowl | Unet++ | http://arxiv.org/abs/1807.10165v1 | Recall | - |
Medical Image Segmentation | 2018 Data Science Bowl | Unet++ | http://arxiv.org/abs/1807.10165v1 | Precision | - |
Medical Image Segmentation | 2018 Data Science Bowl | DCSAU-Net | https://arxiv.org/abs/2202.00972v2 | mIoU | 0.8501 |
Medical Image Segmentation | 2018 Data Science Bowl | DCSAU-Net | https://arxiv.org/abs/2202.00972v2 | Recall | 0.9240 |
Medical Image Segmentation | SegPC-2021 | DCSAU-Net | https://arxiv.org/abs/2202.00972v2 | mIoU | 0.8048 |
Medical Image Segmentation | ISIC2018 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | mean Dice | 90.74 |
Medical Image Segmentation | ISIC2018 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | Accuracy | 94.40 |
Medical Image Segmentation | ISIC2018 | U2netme | https://arxiv.org/abs/2202.00972v2 | mean Dice | 0.905 |
Medical Image Segmentation | ISIC2018 | U2netme | https://arxiv.org/abs/2202.00972v2 | Accuracy | 0.94216 |
Medical Image Segmentation | ISIC2018 | U2netme | https://arxiv.org/abs/2202.00972v2 | Test F1-Score | 0.90604 |
Medical Image Segmentation | ISIC2018 | U2netme | https://arxiv.org/abs/2202.00972v2 | Precision | 0.89502 |
Medical Image Segmentation | ISIC2018 | EMCAD | https://arxiv.org/abs/2405.06880v1 | mean Dice | 0.9096 |
Medical Image Segmentation | ENSeg | YOLOv8-m + SAM-b | https://www.mdpi.com/2076-3417/15/3/1046 | mDice | 0.7877 |
Medical Image Segmentation | CVC-ClinicDB | DUCK-Net | https://arxiv.org/abs/2207.07842v1 | mean Dice | 0.9684 |
Medical Image Segmentation | CVC-ClinicDB | DUCK-Net | https://arxiv.org/abs/2207.07842v1 | mIoU | 0.9343 |
Medical Image Segmentation | CVC-ClinicDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mean Dice | 0.961 |
Medical Image Segmentation | CVC-ClinicDB | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mIoU | 0.926 |
Medical Image Segmentation | CVC-ClinicDB | EMCAD | https://arxiv.org/abs/2405.06880v1 | mean Dice | 0.9521 |
Medical Image Segmentation | CVC-ClinicDB | RaBiT | https://arxiv.org/abs/2307.06420v1 | mean Dice | 0.951 |
Medical Image Segmentation | CVC-ClinicDB | RaBiT | https://arxiv.org/abs/2307.06420v1 | mIoU | 0.911 |
Medical Image Segmentation | CVC-ClinicDB | Yolo-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mean Dice | 0.951 |
Medical Image Segmentation | CVC-ClinicDB | Yolo-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mIoU | 0.909 |
Medical Image Segmentation | CVC-ClinicDB | UGCANet | https://arxiv.org/abs/2307.06260v1 | mean Dice | 0.950 |
Medical Image Segmentation | CVC-ClinicDB | UGCANet | https://arxiv.org/abs/2307.06260v1 | mIoU | 0.907 |
Medical Image Segmentation | CVC-ClinicDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mean Dice | 0.949 |
Medical Image Segmentation | CVC-ClinicDB | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mIoU | 0.907 |
Medical Image Segmentation | CVC-ClinicDB | FCBFormer | https://arxiv.org/abs/2412.13156v1 | mean Dice | 0.9488 |
Medical Image Segmentation | CVC-ClinicDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mean Dice | 0.948 |
Medical Image Segmentation | CVC-ClinicDB | DuAT | https://arxiv.org/abs/2212.11677v1 | mIoU | 0.906 |
Medical Image Segmentation | CVC-ClinicDB | DuAT | https://arxiv.org/abs/2212.11677v1 | Average MAE | 0.006 |
Medical Image Segmentation | CVC-ClinicDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mean Dice | 0.948 |
Medical Image Segmentation | CVC-ClinicDB | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mIoU | 0.902 |
Medical Image Segmentation | CVC-ClinicDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mean Dice | 0.9478 |
Medical Image Segmentation | CVC-ClinicDB | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mIoU | 0.9009 |
Medical Image Segmentation | CVC-ClinicDB | ColonFormer | https://arxiv.org/abs/2205.08473v3 | mean Dice | 0.947 |
Medical Image Segmentation | CVC-ClinicDB | ColonFormer | https://arxiv.org/abs/2205.08473v3 | mIoU | 0.903 |
Medical Image Segmentation | CVC-ClinicDB | FCBFormer | https://arxiv.org/abs/2208.08352v1 | mean Dice | 0.9469 |
Medical Image Segmentation | CVC-ClinicDB | FCBFormer | https://arxiv.org/abs/2208.08352v1 | mIoU | 0.9020 |
Medical Image Segmentation | CVC-ClinicDB | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mean Dice | 0.9468 |
Medical Image Segmentation | CVC-ClinicDB | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.9018 |
Medical Image Segmentation | CVC-ClinicDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mean Dice | 0.9447 |
Medical Image Segmentation | CVC-ClinicDB | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mIoU | 0.8995 |
Medical Image Segmentation | CVC-ClinicDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mean Dice | 0.9434 |
Medical Image Segmentation | CVC-ClinicDB | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mIoU | 0.8998 |
Medical Image Segmentation | CVC-ClinicDB | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mean Dice | 0.942 |
Medical Image Segmentation | CVC-ClinicDB | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mIoU | 0.895 |
Medical Image Segmentation | CVC-ClinicDB | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | mean Dice | 0.9420 |
Medical Image Segmentation | CVC-ClinicDB | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | mean Dice | 0.9419 |
Medical Image Segmentation | CVC-ClinicDB | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | mIoU | 0.9068 |
Medical Image Segmentation | CVC-ClinicDB | MADGNet | https://arxiv.org/abs/2405.06284v1 | mean Dice | 0.9390 |
Medical Image Segmentation | CVC-ClinicDB | MADGNet | https://arxiv.org/abs/2405.06284v1 | mIoU | 0.8950 |
Medical Image Segmentation | CVC-ClinicDB | HarDNet-DFUS | https://arxiv.org/abs/2209.07313v1 | mean Dice | 0.939 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.938 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.894 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | Average MAE | 0.006 |
Medical Image Segmentation | CVC-ClinicDB | ADSNet | https://arxiv.org/abs/2405.07523v1 | mean Dice | 0.938 |
Medical Image Segmentation | CVC-ClinicDB | ADSNet | https://arxiv.org/abs/2405.07523v1 | mIoU | 0.890 |
Medical Image Segmentation | CVC-ClinicDB | Polar U-Net | https://ieeexplore.ieee.org/document/9551998 | mean Dice | 0.9374 |
Medical Image Segmentation | CVC-ClinicDB | Polar U-Net | https://ieeexplore.ieee.org/document/9551998 | mIoU | 0.8977 |
Medical Image Segmentation | CVC-ClinicDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mean Dice | 0.936 |
Medical Image Segmentation | CVC-ClinicDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | mIoU | 0.887 |
Medical Image Segmentation | CVC-ClinicDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | Average MAE | 0.007 |
Medical Image Segmentation | CVC-ClinicDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | S-Measure | 0.954 |
Medical Image Segmentation | CVC-ClinicDB | CaraNet | https://arxiv.org/abs/2108.07368v3 | max E-Measure | 0.991 |
Medical Image Segmentation | CVC-ClinicDB | FANet | https://arxiv.org/abs/2103.17235v3 | mean Dice | 0.9355 |
Medical Image Segmentation | CVC-ClinicDB | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.934 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.933 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.891 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.007 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.956 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.985 |
Medical Image Segmentation | CVC-ClinicDB | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mean Dice | 0.9320 |
Medical Image Segmentation | CVC-ClinicDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mean Dice | 0.931 |
Medical Image Segmentation | CVC-ClinicDB | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mIoU | 0.879 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.93 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.885 |
Medical Image Segmentation | CVC-ClinicDB | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | Average MAE | 0.008 |
Medical Image Segmentation | CVC-ClinicDB | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.926 |
Medical Image Segmentation | CVC-ClinicDB | KDAS | https://arxiv.org/abs/2312.08555v3 | mean Dice | 0.925 |
Medical Image Segmentation | CVC-ClinicDB | KDAS | https://arxiv.org/abs/2312.08555v3 | mIoU | 0.872 |
Medical Image Segmentation | CVC-ClinicDB | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | mean Dice | 0.9239 |
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