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-ClinicDB | ResUNet++ + CRF | https://arxiv.org/abs/2011.07631v2 | mean Dice | 0.9203 |
Medical Image Segmentation | CVC-ClinicDB | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.918 |
Medical Image Segmentation | CVC-ClinicDB | AG-CUResNeSt | https://arxiv.org/abs/2105.00402v3 | mean Dice | 0.9170 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.916 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.871 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.008 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.947 |
Medical Image Segmentation | CVC-ClinicDB | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.979 |
Medical Image Segmentation | CVC-ClinicDB | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.916 |
Medical Image Segmentation | CVC-ClinicDB | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | mean Dice | 0.915 |
Medical Image Segmentation | CVC-ClinicDB | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | mIoU | 0.86 |
Medical Image Segmentation | CVC-ClinicDB | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | F-measure | 0.91 |
Medical Image Segmentation | CVC-ClinicDB | DoubleUnet-DCA | https://arxiv.org/abs/2303.17696v1 | mean Dice | 0.9086 |
Medical Image Segmentation | CVC-ClinicDB | DoubleUnet-DCA | https://arxiv.org/abs/2303.17696v1 | mIoU | 0.8347 |
Medical Image Segmentation | CVC-ClinicDB | ResUNet++ + TTA | https://arxiv.org/abs/2107.12435v1 | mean Dice | 0.9020 |
Medical Image Segmentation | CVC-ClinicDB | ResUNet++ + CRF+ TTA | https://arxiv.org/abs/2107.12435v1 | mean Dice | 0.9017 |
Medical Image Segmentation | CVC-ClinicDB | PraNet | https://arxiv.org/abs/2006.11392v4 | mean Dice | 0.8990 |
Medical Image Segmentation | CVC-ClinicDB | U-Net | http://arxiv.org/abs/1505.04597v1 | mean Dice | 0.8230 |
Medical Image Segmentation | CVC-ClinicDB | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mean Dice | 0.7955 |
Medical Image Segmentation | CVC-ClinicDB | U-Net++ | http://arxiv.org/abs/1807.10165v1 | mean Dice | 0.7940 |
Medical Image Segmentation | CVC-ClinicDB | M3FPolypSegNet | https://arxiv.org/abs/2310.05538v2 | mIoU | 0.8507 |
Medical Image Segmentation | Synapse | nnFormer | https://arxiv.org/abs/2109.03201v6 | Dice score | 0.874 |
Medical Image Segmentation | Autooral dataset | HF-UNet | https://doi.org/10.1038/s41598-024-69125-9 | DSC | 0.7972 |
Medical Image Segmentation | Electron Microscopy Dataset | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | IoU | 87.93 |
Medical Image Segmentation | Electron Microscopy Dataset | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | Dice | 93.55 |
Medical Image Segmentation | Electron Microscopy Dataset | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | AHD95 | 5.3703 |
Medical Image Segmentation | Electron Microscopy Dataset | ReN-UNet | https://arxiv.org/abs/2504.06158v1 | ASD | 0.3047 |
Medical Image Segmentation | CHASE_DB1 | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | DSC | 0.8267 |
Medical Image Segmentation | CHASE_DB1 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | DSC | 0.8251 |
Medical Image Segmentation | CHASE_DB1 | FANet | https://arxiv.org/abs/2103.17235v3 | DSC | 0.8108 |
Medical Image Segmentation | CHAOS MRI Dataset | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | Dice Score | 86.75 |
Medical Image Segmentation | CHAOS MRI Dataset | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | MSD | 66 |
Medical Image Segmentation | CHAOS MRI Dataset | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | VS | 93.85 |
Medical Image Segmentation | MoNuSAC | MaxViT-UNet | https://arxiv.org/abs/2305.08396v5 | Dice | 0.8215 |
Medical Image Segmentation | MoNuSAC | MaxViT-UNet | https://arxiv.org/abs/2305.08396v5 | IoU | 0.7030 |
Medical Image Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8290 |
Medical Image Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.7081 |
Medical Image Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Recall | 0.8281 |
Medical Image Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Specificity | 0.9844 |
Medical Image Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8210 |
Medical Image Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.697 |
Medical Image Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Recall | 0.83 |
Medical Image Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Specificity | 0.9822 |
Medical Image Segmentation | DRIVE | FANet | https://arxiv.org/abs/2103.17235v3 | F1 score | 0.8183 |
Medical Image Segmentation | DRIVE | FANet | https://arxiv.org/abs/2103.17235v3 | mIoU | 0.6927 |
Medical Image Segmentation | DRIVE | FANet | https://arxiv.org/abs/2103.17235v3 | Recall | 0.8215 |
Medical Image Segmentation | DRIVE | FANet | https://arxiv.org/abs/2103.17235v3 | Specificity | 0.9826 |
Medical Image Segmentation | DRIVE | FANet | https://arxiv.org/abs/2103.17235v3 | Precision | 0.8189 |
Medical Image Segmentation | DRIVE | Hi-gMISnet | https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 | mIoU | 0.6901 |
Medical Image Segmentation | DRIVE | BCDU-net | https://arxiv.org/abs/1909.00166v1 | F1 score | 0.8222 |
Medical Image Segmentation | RITE | KiU-Net | https://arxiv.org/abs/2010.01663v2 | Dice | 75.17 |
Medical Image Segmentation | RITE | KiU-Net | https://arxiv.org/abs/2010.01663v2 | Jaccard Index | 60.37 |
Medical Image Segmentation | RITE | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice | 55.24 |
Medical Image Segmentation | RITE | U-Net | http://arxiv.org/abs/1505.04597v1 | Jaccard Index | 31.11 |
Medical Image Segmentation | RITE | SegNet | http://arxiv.org/abs/1511.00561v3 | Dice | 52.23 |
Medical Image Segmentation | RITE | SegNet | http://arxiv.org/abs/1511.00561v3 | Jaccard Index | 39.14 |
Medical Image Segmentation | iSEG 2017 Challenge | HyperDenseNet | http://arxiv.org/abs/1804.02967v2 | Dice Score | 0.9257 |
Medical Image Segmentation | Extended Task10_Colon Medical Decathlon | nnUNet | https://arxiv.org/abs/2407.21516v1 | Average Dice | 0.6988 |
Medical Image Segmentation > Lesion Segmentation | PH2 | IARS SegNet | https://arxiv.org/abs/2310.20292v1 | Dice Score | 0.9712 |
Medical Image Segmentation > Lesion Segmentation | PH2 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | Dice Score | 0.9570 |
Medical Image Segmentation > Lesion Segmentation | PH2 | DermoSegDiff-B | https://arxiv.org/abs/2308.02959v1 | Dice Score | 0.9467 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | Polar Res-U-Net++ | https://ieeexplore.ieee.org/document/9551998 | mean Dice | 0.9253 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | DuAT | https://arxiv.org/abs/2212.11677v1 | mean Dice | 0.923 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | DuAT | https://arxiv.org/abs/2212.11677v1 | Mean IoU | 0.867 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | ProMISe | https://arxiv.org/abs/2403.04164v3 | mean Dice | 0.921 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | ProMISe | https://arxiv.org/abs/2403.04164v3 | Mean IoU | 0.850 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | RMSM UNet + DF-RAM +EF-RAM | https://arxiv.org/abs/2111.08708v3 | mean Dice | 0.9152 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | BAT | https://arxiv.org/abs/2110.03864v1 | mean Dice | 0.912 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | BAT | https://arxiv.org/abs/2110.03864v1 | Mean IoU | 0.843 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mean Dice | 0.911 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | Mean IoU | 0.841 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | mean Dice | 0.9074 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | Mean IoU | 0.8456 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | DermoSegDiff-A | https://arxiv.org/abs/2308.02959v1 | mean Dice | 0.9005 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | DoubleU-Net | https://arxiv.org/abs/2006.04868v2 | mean Dice | 0.8962 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | MCGU-Net | https://arxiv.org/abs/2003.05056v1 | mean Dice | 0.895 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | mean Dice | 0.8813 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | Attn U-Net + Multi-Input + FTL | http://arxiv.org/abs/1810.07842v1 | mean Dice | 0.856 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | AIM++ (256x256, 1.5m parameters, 10% labeled data, no pretraining) | https://arxiv.org/abs/2401.14387v2 | mean Dice | 0.85 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | BCDU-net | https://arxiv.org/abs/1909.00166v1 | mean Dice | 0.847 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | U-Net + FTL | http://arxiv.org/abs/1810.07842v1 | mean Dice | 0.829 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | Attn U-Net + DL | http://arxiv.org/abs/1810.07842v1 | mean Dice | 0.806 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 | BCDU-Net (d=3) | https://arxiv.org/abs/1909.00166v1 | F1-Score | 0.851 |
Medical Image Segmentation > Lesion Segmentation | BUS 2017 Dataset B | Attn U-Net + Multi-Input + FTL | http://arxiv.org/abs/1810.07842v1 | Dice Score | 0.804 |
Medical Image Segmentation > Lesion Segmentation | BUS 2017 Dataset B | Salient Attention U-Net | https://arxiv.org/abs/1910.08978v2 | Dice Score | 0.7341 |
Medical Image Segmentation > Lesion Segmentation | BUS 2017 Dataset B | U-Net + FTL | http://arxiv.org/abs/1810.07842v1 | Dice Score | 0.669 |
Medical Image Segmentation > Lesion Segmentation | BUS 2017 Dataset B | Attn U-Net + DL | http://arxiv.org/abs/1810.07842v1 | Dice Score | 0.615 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2018 Task 1 | DCSAU-Net | https://arxiv.org/abs/2202.00972v2 | mIoU | 0.8301 |
Medical Image Segmentation > Lesion Segmentation | ISIC 2017 | Automatic skin lesion segmentation with fully convolutional-deconvolutional networks | http://arxiv.org/abs/1703.05165v2 | Mean IoU | 0.765 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | D-UNet | https://arxiv.org/abs/1908.05104v1 | Dice | 0.5349 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | D-UNet | https://arxiv.org/abs/1908.05104v1 | Precision | 0.6331 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | D-UNet | https://arxiv.org/abs/1908.05104v1 | Recall | 0.5243 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | 2D Dense-UNet | http://arxiv.org/abs/1709.07330v3 | Dice | 0.4741 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | 2D Dense-UNet | http://arxiv.org/abs/1709.07330v3 | IoU | 0.3559 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | 2D Dense-UNet | http://arxiv.org/abs/1709.07330v3 | Precision | 0.5613 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | 2D Dense-UNet | http://arxiv.org/abs/1709.07330v3 | Recall | 0.4875 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | DeepLab v3+ | http://arxiv.org/abs/1802.02611v3 | Dice | 0.4609 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | DeepLab v3+ | http://arxiv.org/abs/1802.02611v3 | IoU | 0.3458 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | DeepLab v3+ | http://arxiv.org/abs/1802.02611v3 | Precision | 0.5831 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice | 0.4606 |
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