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Medical Image Segmentation On Glas

Metriken

Dice

F1

IoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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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