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Found 4 papers, 4 papers with code
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
The proposed ScribFormer model has a triple-branch structure, i. e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch.
Ranked #1 on
Semantic Segmentation
on ACDC Scribbles
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19.
Ranked #1 on
Medical Image Segmentation
on MosMedData
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis.
Ranked #5 on
Semantic Segmentation
on ACDC Scribbles
LViT: Language meets Vision Transformer in Medical Image Segmentation
In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data.
Ranked #3 on
Medical Image Segmentation
on MoNuSeg
