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 > Retinal Vessel Segmentation | DRIVE | IterNet | https://arxiv.org/abs/1912.05763v1 | AUC | 0.9816 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | VGN | http://arxiv.org/abs/1806.02279v1 | F1 score | 0.8263 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | VGN | http://arxiv.org/abs/1806.02279v1 | AUC | 0.9802 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DUNet | http://arxiv.org/abs/1811.01206v1 | F1 score | 0.8237 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DUNet | http://arxiv.org/abs/1811.01206v1 | AUC | 0.9802 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | LadderNet | https://arxiv.org/abs/1810.07810v4 | F1 score | 0.8202 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | LadderNet | https://arxiv.org/abs/1810.07810v4 | AUC | 0.9793 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | BCDU-Net (d=3) | https://arxiv.org/abs/1909.00166v1 | F1 score | 0.8224 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | BCDU-Net (d=3) | https://arxiv.org/abs/1909.00166v1 | AUC | 0.9789 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | F1 score | 0.8149 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | AUC | 0.9779 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | CE-Net | http://arxiv.org/abs/1903.02740v1 | AUC | 0.9779 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | CE-Net | http://arxiv.org/abs/1903.02740v1 | Accuracy | 0.9545 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | U-Net | http://arxiv.org/abs/1505.04597v1 | F1 score | 0.8142 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.9755 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8290 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Accuracy | 0.9707 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.7081 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | sensitivity | 0.8281 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Specificity | 0.9844 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | ConvMixer | https://www.mdpi.com/2076-3417/13/7/4445 | F1 score | 0.8245 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | ConvMixer-Light | https://www.mdpi.com/2076-3417/13/7/4445 | F1 score | 0.8215 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8210 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Accuracy | 0.9689 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.6970 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | sensitivity | 0.83 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Specificity | 0.9822 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | F1 score | 0.75 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | Accuracy | 0.9593 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | sensitivity | 0.7119 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | Specificity | 0.9832 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | ET-Net | https://arxiv.org/abs/1907.10936v1 | Accuracy | 0.956 |
Medical Image Segmentation > Retinal Vessel Segmentation | DRIVE | ET-Net | https://arxiv.org/abs/1907.10936v1 | mIoU | 0.7744 |
Medical Image Segmentation > Retinal Vessel Segmentation | INSPIRE-AVR (LUNet subset) | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice | 75.6 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 83.2 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Junior Ophtalmologist | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 82.6 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | VascX | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 80.6 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Automorph | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 74.0 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Little W-Net | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 60.9 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | HRF | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9783 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | RITE/DRIVE | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9666 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | LES-AV | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9481 |
Medical Image Segmentation > Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | INSPIRE-AVR (LUNet subset) | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 75.6 |
Medical Image Segmentation > 3D Medical Imaging Segmentation | TCIA Pancreas-CT | Holistic-nested CNN | http://arxiv.org/abs/1702.00045v1 | Dice Score | 81.3 |
Medical Image Segmentation > 3D Medical Imaging Segmentation | TCIA Pancreas-CT | Multi-class 3D FCN | http://arxiv.org/abs/1803.05431v2 | Dice Score | 76.8 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | CT-150 | Att U-Net | http://arxiv.org/abs/1804.03999v3 | Precision | 0.849 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | CT-150 | Att U-Net | http://arxiv.org/abs/1804.03999v3 | Recall | 0.841 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | CT-150 | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice Score | 0.814 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | CT-150 | U-Net | http://arxiv.org/abs/1505.04597v1 | Precision | 0.848 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | CT-150 | U-Net | http://arxiv.org/abs/1505.04597v1 | Recall | 0.806 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | Pancreas-CT | PanSAM | https://openreview.net/forum?id=4pn1Enab5Q | Dice | 87.01 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | TCIA Pancreas-CT Dataset | Recurrent Saliency Transformation Network | http://arxiv.org/abs/1709.04518v4 | Dice Score | 0.845 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | TCIA Pancreas-CT Dataset | Att U-Net | http://arxiv.org/abs/1804.03999v3 | Dice Score | 0.831 |
Medical Image Segmentation > 3D Medical Imaging Segmentation > Pancreas Segmentation | TCIA Pancreas-CT Dataset | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice Score | 0.82 |
Medical Image Segmentation > Volumetric Medical Image Segmentation | PROMISE 2012 | V-Net + Dice-based loss | http://arxiv.org/abs/1606.04797v1 | Dice Score | 0.869 |
Medical Image Segmentation > Volumetric Medical Image Segmentation | PROMISE 2012 | Fully-connected CRF | http://arxiv.org/abs/1807.07464v1 | Dice Score | 0.780 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | Polar U-Net | https://ieeexplore.ieee.org/document/9551998 | IoU | 89.85 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | Polar U-Net | https://ieeexplore.ieee.org/document/9551998 | Dice | 93.02 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | KiU-Net 3D Liver | https://arxiv.org/abs/2010.01663v2 | IoU | 89.46 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | Semantic Genesis | https://arxiv.org/abs/2007.06959v1 | IoU | 85.6 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | Semantic Genesis | https://arxiv.org/abs/2007.06959v1 | Dice | 92.27 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | ModelGenesis | https://arxiv.org/abs/1908.06912v1 | IoU | 79.52 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | ModelGenesis | https://arxiv.org/abs/1908.06912v1 | Dice | 91.13 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | PVTFormer | https://arxiv.org/abs/2401.09630v3 | IoU | 78.46 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | PVTFormer | https://arxiv.org/abs/2401.09630v3 | Dice | 86.78 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | PVTFormer | https://arxiv.org/abs/2401.09630v3 | HD | 3.50 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | H-DenseUnet Liver | http://arxiv.org/abs/1709.07330v3 | Dice | 96.5 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | KiU-Net 3D | https://arxiv.org/abs/2010.01663v2 | Dice | 94.23 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | U-Net LiS (MICCAI 17) | https://arxiv.org/abs/1905.03639v1 | Dice | 94 |
Medical Image Segmentation > Liver Segmentation | LiTS2017 | H-DenseUnet Lession | http://arxiv.org/abs/1709.07330v3 | Dice | 82.4 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | YOLO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | S measure | 0.9 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | YOLO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mean E-measure | 93.8 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | YOLO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mean F-measure | 93.8 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | YOLO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | Dice | 0.90 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | YOLO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | Sensitivity | 83.7 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | LGRNet | https://arxiv.org/abs/2407.05703v1 | Dice | 0.853 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | LGRNet | https://arxiv.org/abs/2407.05703v1 | mean IoU | 0.783 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | S measure | 0.870 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | mean E-measure | 0.920 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | weighted F-measure | 0.794 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | mean F-measure | 0.831 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | Dice | 0.825 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | SALI | https://arxiv.org/abs/2406.13532v1 | Sensitivity | 0.811 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | S measure | 0.806 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | mean E-measure | 0.798 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | weighted F-measure | 0.676 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | mean F-measure | 0.730 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | Dice | 0.756 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | PNS+ | https://arxiv.org/abs/2203.14291v3 | Sensitivity | 0.630 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | S measure | 0.815 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | mean E-measure | 0.855 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | weighted F-measure | 0.716 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | mean F-measure | 0.774 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | Dice | 0.753 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | AutoSAM | https://arxiv.org/abs/2306.06370v1 | Sensitivity | 0.672 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | 2/3D | null | S measure | 0.786 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | 2/3D | null | mean E-measure | 0.777 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | 2/3D | null | weighted F-measure | 0.652 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | 2/3D | null | mean F-measure | 0.708 |
Medical Image Segmentation > Video Polyp Segmentation | SUN-SEG-Easy (Unseen) | 2/3D | null | Dice | 0.722 |
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