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 > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | U-Net | http://arxiv.org/abs/1505.04597v1 | IoU | 0.3447 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | U-Net | http://arxiv.org/abs/1505.04597v1 | Precision | 0.5994 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | U-Net | http://arxiv.org/abs/1505.04597v1 | Recall | 0.4449 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | PSPNet | http://arxiv.org/abs/1612.01105v2 | Dice | 0.3571 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | PSPNet | http://arxiv.org/abs/1612.01105v2 | IoU | 0.254 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | PSPNet | http://arxiv.org/abs/1612.01105v2 | Precision | 0.4769 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | PSPNet | http://arxiv.org/abs/1612.01105v2 | Recall | 0.3335 |
Medical Image Segmentation > Lesion Segmentation | null | STPNet | https://arxiv.org/abs/2504.01561v1 | Dice | 76.18 |
Medical Image Segmentation > Lesion Segmentation | null | CMIRNet | https://arxiv.org/abs/2504.01561v1 | Dice | 73.69 |
Medical Image Segmentation > Lesion Segmentation | null | LAVT | https://arxiv.org/abs/2504.01561v1 | Dice | 73.29 |
Medical Image Segmentation > Lesion Segmentation | null | SAM2UNet | https://arxiv.org/abs/2504.01561v1 | Dice | 72.91 |
Medical Image Segmentation > Lesion Segmentation | null | nnUNet | https://arxiv.org/abs/2504.01561v1 | Dice | 72.59 |
Medical Image Segmentation > Lesion Segmentation | null | GLoRIA | https://arxiv.org/abs/2504.01561v1 | Dice | 72.42 |
Medical Image Segmentation > Lesion Segmentation | null | ViLT | https://arxiv.org/abs/2504.01561v1 | Dice | 72.36 |
Medical Image Segmentation > Lesion Segmentation | null | ConVIRT | https://arxiv.org/abs/2504.01561v1 | Dice | 72.06 |
Medical Image Segmentation > Lesion Segmentation | null | CLIP | https://arxiv.org/abs/2504.01561v1 | Dice | 71.97 |
Medical Image Segmentation > Lesion Segmentation | null | UNet++ | https://arxiv.org/abs/2504.01561v1 | Dice | 71.75 |
Medical Image Segmentation > Lesion Segmentation | null | UniLSeg | https://arxiv.org/abs/2504.01561v1 | Dice | 71.6 |
Medical Image Segmentation > Lesion Segmentation | null | TransUNet | https://arxiv.org/abs/2504.01561v1 | Dice | 71.24 |
Medical Image Segmentation > Lesion Segmentation | null | TGANet | https://arxiv.org/abs/2504.01561v1 | Dice | 70.29 |
Medical Image Segmentation > Lesion Segmentation | null | UCTransNet | https://arxiv.org/abs/2504.01561v1 | Dice | 66.74 |
Medical Image Segmentation > Lesion Segmentation | null | AttUNet | https://arxiv.org/abs/2504.01561v1 | Dice | 65.57 |
Medical Image Segmentation > Lesion Segmentation | null | Swin-UNet | https://arxiv.org/abs/2504.01561v1 | Dice | 63.29 |
Medical Image Segmentation > Lesion Segmentation | null | UNet | https://arxiv.org/abs/2504.01561v1 | Dice | 62.96 |
Medical Image Segmentation > Lesion Segmentation | ISLES-2015 | 3D CNN + CRF | http://arxiv.org/abs/1603.05959v3 | Dice Score | 59.0% |
Medical Image Segmentation > Lesion Segmentation | University of Waterloo skin cancer database | DTP-Net | https://www.sciencedirect.com/science/article/pii/S0010482522006047 | Dice score | 0.884 ±0.100 |
Medical Image Segmentation > Lesion Segmentation | University of Waterloo skin cancer database | DeepLabV3+ | http://arxiv.org/abs/1807.08891v1 | Dice score | 0.883 ±0.108 |
Medical Image Segmentation > Lesion Segmentation | University of Waterloo skin cancer database | FCN-8s | http://arxiv.org/abs/1503.02351v1 | Dice score | 0.870 ±0.063 |
Medical Image Segmentation > Lesion Segmentation | University of Waterloo skin cancer database | SegNet | http://arxiv.org/abs/1511.00561v3 | Dice score | 0.854 ±0.088 |
Medical Image Segmentation > Lesion Segmentation | University of Waterloo skin cancer database | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice score | 0.836 ±0.132 |
Medical Image Segmentation > Lesion Segmentation | HAM10000 | DermoSegDiff-B | https://arxiv.org/abs/2308.02959v1 | Dice Score | 0.943 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | MGA-Net | https://www.sciencedirect.com/science/article/pii/S1568494622006299#sec4 | IoU | 0.3803 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | MGA-Net | https://www.sciencedirect.com/science/article/pii/S1568494622006299#sec4 | Precision | 0.6773 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | MGA-Net | https://www.sciencedirect.com/science/article/pii/S1568494622006299#sec4 | Recall | 0.4525 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | X-Net | https://arxiv.org/abs/1907.07000v2 | Dice | 0.4867 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | X-Net | https://arxiv.org/abs/1907.07000v2 | IoU | 0.3723 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | X-Net | https://arxiv.org/abs/1907.07000v2 | Precision | 0.6000 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | X-Net | https://arxiv.org/abs/1907.07000v2 | Recall | 0.4752 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | ResUNet | http://arxiv.org/abs/1711.10684v1 | Dice | 0.4702 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | ResUNet | http://arxiv.org/abs/1711.10684v1 | IoU | 0.3549 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | ResUNet | http://arxiv.org/abs/1711.10684v1 | Precision | 0.5941 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | ResUNet | http://arxiv.org/abs/1711.10684v1 | Recall | 0.4537 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | SegNet | http://arxiv.org/abs/1511.00561v3 | Dice | 0.2767 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | SegNet | http://arxiv.org/abs/1511.00561v3 | IoU | 0.1911 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | SegNet | http://arxiv.org/abs/1511.00561v3 | Precision | 0.3938 |
Medical Image Segmentation > Lesion Segmentation | Anatomical Tracings of Lesions After Stroke (ATLAS) | SegNet | http://arxiv.org/abs/1511.00561v3 | Recall | 0.2532 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2013 leaderboard | InputCascadeCNN | http://arxiv.org/abs/1505.03540v3 | Dice Score | 0.84 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2013 leaderboard | SegAN | http://arxiv.org/abs/1706.01805v2 | Dice Score | 0.84 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2017 val | SegFormer3D | https://arxiv.org/abs/2404.10156v2 | Dice Score | 0.9096 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2017 val | SegFormer3D | https://arxiv.org/abs/1906.01796v2 | Dice Score | 0.9071 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2017 val | Wang et al. | http://arxiv.org/abs/1709.00382v2 | Dice Score | 0.905 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2013 | Semantic Genesis | https://arxiv.org/abs/2007.06959v1 | Dice Score | 92.76 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2013 | ModelGenesis | https://arxiv.org/abs/1908.06912v1 | Dice Score | 0.9258 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2013 | InputCascadeCNN | http://arxiv.org/abs/1505.03540v3 | Dice Score | 0.88 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score ET | 0.692 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score TC | 0.918 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score WT | 0.926 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score NET | 0.859 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score CC | 0.715 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | Dice Score ED | 0.884 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | HD95_min ET | 53.87 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | HD95_min NET | 8.01 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | HD95_min CC | 96.41 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | HD95_min TC | 7.46 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTs Peds 2024 | CNMC_PMILAB | https://arxiv.org/abs/2412.04094v3 | HD95_min WT | 7.14 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Segtran (i3d) | https://arxiv.org/abs/2105.09511v3 | TC | 0.817 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Segtran (i3d) | https://arxiv.org/abs/2105.09511v3 | Avg. | 0.817 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Extension of nnU-Net | https://arxiv.org/abs/2105.09511v3 | ET | 0.740 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Extension of nnU-Net | https://arxiv.org/abs/2105.09511v3 | WT | 0.894 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Extension of nnU-Net | https://arxiv.org/abs/2105.09511v3 | TC | 0.807 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Extension of nnU-Net | https://arxiv.org/abs/2105.09511v3 | Avg. | 0.812 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Bag of tricks | https://arxiv.org/abs/2105.09511v3 | ET | 0.729 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Bag of tricks | https://arxiv.org/abs/2105.09511v3 | WT | 0.895 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Bag of tricks | https://arxiv.org/abs/2105.09511v3 | TC | 0.802 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | Residual 3D U-Net ET | https://arxiv.org/abs/2103.04430v2 | Dice Score | 0.7163 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2019 | 3D U-Net [6] ET | https://arxiv.org/abs/2103.04430v2 | Dice Score | 0.6876 |
Medical Image Segmentation > Brain Tumor Segmentation | BRISC | Swin-HAFNey | https://arxiv.org/abs/2506.14318v1 | mIoU | 82 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2014 | Cascaded Anisotropic CNNs | http://arxiv.org/abs/1709.00382v2 | Dice Score | 0.8739 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | Dice Score ET | 0.87 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | Dice Score TC | 0.865 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | Dice Score WT | 0.926 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | HD95_min ET | 0.20745 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | HD95_min TC | 0.2395 |
Medical Image Segmentation > Brain Tumor Segmentation | BraTS-Africa | CNMC_PMILAB | https://arxiv.org/abs/2412.04111v2 | HD95_min WT | 0.04003 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | NVDLMED | http://arxiv.org/abs/1810.11654v3 | Dice Score | 0.87049 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | Dice Score | 0.8037 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | MSD | 0.9 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | MS-Dual-Guided | https://arxiv.org/abs/1906.02849v3 | VS | 93.08 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | 3D-DDA | https://ieeexplore.ieee.org/document/10222602 | WT | 0.9135 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | 3D-DDA | https://ieeexplore.ieee.org/document/10222602 | TC | 0.8677 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | 3D-DDA | https://ieeexplore.ieee.org/document/10222602 | ET | 0.8069 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 | Semantic Genesis | https://arxiv.org/abs/2007.06959v1 | IoU | 68.8 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS 2018 val | OM-Net + CGAp | https://arxiv.org/abs/1906.01796v2 | Dice Score | 91.59 |
Medical Image Segmentation > Brain Tumor Segmentation | 768 chest X-ray images | tumor | https://arxiv.org/abs/2107.12046v1 | 1:3 Accuracy | 8 |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2015 | OM-Net + CGAp | https://arxiv.org/abs/1906.01796v2 | Dice Score | 87% |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2015 | CNN + 3D filters | http://arxiv.org/abs/1701.03056v2 | Dice Score | 85.0% |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2015 | 3D CNN + CRF | http://arxiv.org/abs/1603.05959v3 | Dice Score | 85.0% |
Medical Image Segmentation > Brain Tumor Segmentation | BRATS-2015 | AFN-6 | http://arxiv.org/abs/1805.08403v3 | Dice Score | 84% |
Medical Image Segmentation > Cell Segmentation | DIC-C2DH-HeLa | EncLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.793 |
Medical Image Segmentation > Cell Segmentation | DIC-C2DH-HeLa | DecLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.511 |
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