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Medical Image Segmentation On Glas

Metrics

Dice

F1

IoU

Results

Performance results of various models on this benchmark

Model Name	Dice	F1	IoU	Paper Title	Repository
MDM	91.95	91.95	85.13	Masked Diffusion as Self-supervised Representation Learner
HistoSeg	-	-	76.73	HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
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
U-Net	85.45	85.45	74.78	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
UCTransNet	90.18	90.18	82.96	UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
U-Net	76.26	76.26	63.03	Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
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
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

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Medical Image Segmentation On Glas | SOTA | HyperAI