Medical Image Segmentation On Glas

Metrics

Dice

F1

IoU

Results

Performance results of various models on this benchmark

				Paper Title
Hi-gMISnet	93.25	93.25	-	Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN
MDM	91.95	91.95	85.13	Masked Diffusion as Self-supervised Representation Learner
UCTransNet	90.18	90.18	82.96	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Trans2Unet	89.84	89.84	82.54	Trans2Unet: Neural fusion for Nuclei Semantic Segmentation
U-Net++	87.56	87.56	79.13	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
U-Net	85.45	85.45	74.78	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
MedT	81.02	81.02	69.61	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
LoGo	79.68	79.68	67.69	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
U-Net	76.26	76.26	63.03	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
HistoSeg	-	-	76.73	HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

0 of 10 row(s) selected.

Medical Image Segmentation On Glas

Metrics

Dice

F1

IoU

Results

Performance results of various models on this benchmark

				Paper Title
Hi-gMISnet	93.25	93.25	-	Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN
MDM	91.95	91.95	85.13	Masked Diffusion as Self-supervised Representation Learner
UCTransNet	90.18	90.18	82.96	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Trans2Unet	89.84	89.84	82.54	Trans2Unet: Neural fusion for Nuclei Semantic Segmentation
U-Net++	87.56	87.56	79.13	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
U-Net	85.45	85.45	74.78	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
MedT	81.02	81.02	69.61	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
LoGo	79.68	79.68	67.69	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
U-Net	76.26	76.26	63.03	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
HistoSeg	-	-	76.73	HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

0 of 10 row(s) selected.