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 > Cell Segmentation | MoNuSeg | PromptNu | https://github.com/NucleiDet/PromptNu | Average Dice | 0.838 |
Medical Image Segmentation > Cell Segmentation | STARE | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.7756 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DH-SIM+ | EncLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.811 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DH-SIM+ | DecLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.802 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Cascade Mask RCNN-ResNest-200 | https://www.nature.com/articles/s41592-021-01249-6 | mask AP | 47.9 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Cascade Mask RCNN-ResNest-200 | https://www.nature.com/articles/s41592-021-01249-6 | mask AFNR | 45.3 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Cascade Mask RCNN-ResNest-200 | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Transferability | 0.98 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Cascade Mask RCNN-ResNest-200 | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Extrapolation (A549) | 1403 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Cascade Mask RCNN-ResNest-200 | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Extrapolation (A172) | 1328 |
Medical Image Segmentation > Cell Segmentation | LIVECell | CenterMask-VoVNet2-FPN | https://www.nature.com/articles/s41592-021-01249-6 | mask AP | 47.8 |
Medical Image Segmentation > Cell Segmentation | LIVECell | CenterMask-VoVNet2-FPN | https://www.nature.com/articles/s41592-021-01249-6 | mask AFNR | 52.2 |
Medical Image Segmentation > Cell Segmentation | LIVECell | CenterMask-VoVNet2-FPN | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Transferability | 1.21 |
Medical Image Segmentation > Cell Segmentation | LIVECell | CenterMask-VoVNet2-FPN | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Extrapolation (A549) | 2031 |
Medical Image Segmentation > Cell Segmentation | LIVECell | CenterMask-VoVNet2-FPN | https://www.nature.com/articles/s41592-021-01249-6 | LIVECell Extrapolation (A172) | 1948 |
Medical Image Segmentation > Cell Segmentation | LIVECell | Point2Mask | https://link.springer.com/chapter/10.1007/978-3-031-12053-4_11 | mask AP | 43.53 |
Medical Image Segmentation > Cell Segmentation | Fluo-C3DL-MDA231 | Dual U-Net (Neighbor distances) | https://arxiv.org/abs/2004.01486v4 | SEG (~Mean IoU) | 0.616 |
Medical Image Segmentation > Cell Segmentation | CoNSeP | PromptNu | https://github.com/NucleiDet/PromptNu | Average Dice | 0.857 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DL-HeLa | Dual U-Net (Neighbor distances) | https://arxiv.org/abs/2004.01486v4 | SEG (~Mean IoU) | 0.895 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DL-HeLa | DecLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.839 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DL-HeLa | EncLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.811 |
Medical Image Segmentation > Cell Segmentation | PanNuke | PromptNu | https://github.com/NucleiDet/PromptNu | Average Dice | 0.860 |
Medical Image Segmentation > Cell Segmentation | EVICAN | DeepCeNS | https://ieeexplore.ieee.org/abstract/document/9533624 | mask AP | 52.56 |
Medical Image Segmentation > Cell Segmentation | EVICAN | DeepCeNS | https://ieeexplore.ieee.org/abstract/document/9533624 | mask AP50 | 83.40 |
Medical Image Segmentation > Cell Segmentation | EVICAN | EVICAN-MRCNN | https://pubmed.ncbi.nlm.nih.gov/32239126/ | mask AP | 32.20 |
Medical Image Segmentation > Cell Segmentation | EVICAN | EVICAN-MRCNN | https://pubmed.ncbi.nlm.nih.gov/32239126/ | mask AP50 | 61.58 |
Medical Image Segmentation > Cell Segmentation | PhC-C2DH-U373 | EncLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.842 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DH-GOWT1 | DecLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.854 |
Medical Image Segmentation > Cell Segmentation | Fluo-N2DH-GOWT1 | EncLSTM | http://arxiv.org/abs/1805.11247v2 | SEG (~Mean IoU) | 0.85 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | MM-WHS 2017 | ACINet | https://arxiv.org/abs/2209.00123v1 | DSC | 81.5 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | MM-WHS 2017 | ICT-MedSeg | https://arxiv.org/abs/2202.00677v2 | DSC | 79.83 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 5% labeled data | AD-MT | https://arxiv.org/abs/2311.17325v2 | Dice (Average) | 88.75 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 5% labeled data | CrossMatch | https://arxiv.org/abs/2405.00354v2 | Dice (Average) | 88.27 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 5% labeled data | UniMatch | https://arxiv.org/abs/2208.09910v2 | Dice (Average) | 87.61 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 5% labeled data | BCP | https://arxiv.org/abs/2305.00673v1 | Dice (Average) | 87.59 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 10% labeled data | SDCL | https://arxiv.org/abs/2409.16728v2 | Dice (Average) | 90.92 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 10% labeled data | UniMatch | https://arxiv.org/abs/2208.09910v2 | Dice (Average) | 89.92 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 10% labeled data | BCPCauSSL | http://openaccess.thecvf.com//content/ICCV2023/html/Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper.html | Dice (Average) | 89.66 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 10% labeled data | PatchCL | http://openaccess.thecvf.com//content/CVPR2023/html/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | Dice (Average) | 89.10 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 10% labeled data | BCP | https://arxiv.org/abs/2305.00673v1 | Dice (Average) | 88.84 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 20% labeled data | PatchCL | http://openaccess.thecvf.com//content/CVPR2023/html/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | Dice (Average) | 91.20 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 20% labeled data | UniMatch | https://arxiv.org/abs/2208.09910v2 | Dice (Average) | 90.47 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 20% labeled data | BCPCauSSL | http://openaccess.thecvf.com//content/ICCV2023/html/Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper.html | Dice (Average) | 89.99 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | ACDC 20% labeled data | BCP | https://arxiv.org/abs/2305.00673v1 | Dice (Average) | 89.52 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | LA 5% labeled data | AD-MT | https://arxiv.org/abs/2311.17325v2 | Average Dice | 89.63 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | Lesion Segmentation on ISIC 2018 | AIM++ (256x256, 1.5m parameters, 10% labeled data, no pretraining) | https://arxiv.org/abs/2401.14387v2 | Dice Score | 0.85 |
Medical Image Segmentation > Semi-supervised Medical Image Segmentation | Pancreas-CT 10% labeled data | AD-MT | https://arxiv.org/abs/2311.17325v2 | Dice (Average) | 80.21 |
Medical Image Segmentation > Skin Lesion Segmentation | ISIC 2017 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Mean IoU | 97.4 |
Medical Image Segmentation > Skin 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 > Skin Lesion Segmentation | University of Waterloo skin cancer database | DeepLabV3+ | null | Dice Score | 0.883 ±0.108 |
Medical Image Segmentation > Skin Lesion Segmentation | University of Waterloo skin cancer database | FCN-8s | null | Dice Score | 0.870 ±0.063 |
Medical Image Segmentation > Skin Lesion Segmentation | University of Waterloo skin cancer database | SegNet | null | Dice Score | 0.854 ±0.088 |
Medical Image Segmentation > Skin Lesion Segmentation | University of Waterloo skin cancer database | U-Net | null | Dice Score | 0.836 ±0.132 |
Medical Image Segmentation > Skin Lesion Segmentation | ISIC2016 | QTSeg | https://arxiv.org/abs/2412.17241v1 | MAE | 0.0359 |
Medical Image Segmentation > Skin Lesion Segmentation | ISIC2016 | QTSeg | https://arxiv.org/abs/2412.17241v1 | ACC | 96.41 |
Medical Image Segmentation > Skin Lesion Segmentation | ISIC2016 | QTSeg | https://arxiv.org/abs/2412.17241v1 | Dice | 92.42 |
Medical Image Segmentation > Skin Lesion Segmentation | ISIC2016 | QTSeg | https://arxiv.org/abs/2412.17241v1 | Average IOU | 86.74 |
Medical Image Segmentation > MRI segmentation > Brain Tumor Classification | Brain Tumor MRI Dataset | Extra-tree | http://dx.doi.org/10.1016/j.jcmds.2024.100103 | 1:1 Accuracy | 97.28 |
Medical Image Segmentation > Brain Segmentation | Brain MRI segmentation | SynthSeg | https://arxiv.org/abs/2003.01995v3 | Dice Score | 0.8690000000000001 |
Medical Image Segmentation > Brain Segmentation | Brain MRI segmentation | SynthSeg | https://arxiv.org/abs/2003.01995v3 | Dice Scoe | 0.861 |
Medical Image Segmentation > Brain Segmentation | Brain MRI segmentation | U-Net | https://arxiv.org/abs/1906.03720v1 | Dice Score | 0.82 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | AUC | 0.9874 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | F1 score | 0.8156 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | mIoU | 0.8308 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Acc | 0.9710 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Sensitivity | 0.8361 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | MCC | 0.8012 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | AUC | 0.9845 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | MCC | 0.8012 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | 1:1 Accuracy | 0.9723 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | DSC | 0.8289 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | Average IOU | 0.7089 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | VGN | http://arxiv.org/abs/1806.02279v1 | AUC | 0.9838 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | VGN | http://arxiv.org/abs/1806.02279v1 | F1 score | 0.8151 |
Medical Image Segmentation > Retinal Vessel Segmentation | HRF | U-Net ASPP | https://arxiv.org/abs/2307.14179v1 | mIoU | 0.8966 |
Medical Image Segmentation > Retinal Vessel Segmentation | ROSE-2 | OCTAve: OCTA-Net | https://arxiv.org/abs/2207.12238v1 | Dice Score | 71.18 |
Medical Image Segmentation > Retinal Vessel Segmentation | ROSE-2 | OCTA-Net | https://arxiv.org/abs/2007.05201v2 | Dice Score | 70.77 |
Medical Image Segmentation > Retinal Vessel Segmentation | ROSE-2 | CE-Net | http://arxiv.org/abs/1903.02740v1 | Dice Score | 70.66 |
Medical Image Segmentation > Retinal Vessel Segmentation | ROSE-2 | ResU-Net | http://arxiv.org/abs/1711.10684v1 | Dice Score | 67.25 |
Medical Image Segmentation > Retinal Vessel Segmentation | ROSE-2 | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice Score | 65.64 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | F1 score | 0.8101 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | AUC | 0.9937 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | mIOU | 0.8268 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Sensitivity | 0.8599 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Acc | 0.9751 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | MCC | 0.7989 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | F1 score | 0.8271 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | AUC | 0.9920 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | Sensitivity | 0.8690 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | F1 score | 0.8957 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | AUC | 0.9914 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | mIOU | 0.9705 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | Sensitivity | 0.8199 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | F1 score | 0.8151 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | AUC | 0.9913 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | Sensitivity | 0.8798 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | SA-UNet | https://arxiv.org/abs/2004.03696v3 | F1 score | 0.8153 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | SA-UNet | https://arxiv.org/abs/2004.03696v3 | AUC | 0.9905 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | IterNet | https://arxiv.org/abs/1912.05763v1 | F1 score | 0.8073 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | IterNet | https://arxiv.org/abs/1912.05763v1 | AUC | 0.9851 |
Medical Image Segmentation > Retinal Vessel Segmentation | CHASE_DB1 | LadderNet | https://arxiv.org/abs/1810.07810v4 | F1 score | 0.8031 |
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