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Found 3 papers, 2 papers with code
A Recent Survey of Vision Transformers for Medical Image Segmentation
This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation.
MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation
The proposed Hybrid Decoder, based on MaxViT-block, is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage with a nominal memory and computational burden.
Ranked #1 on
Medical Image Segmentation
on MoNuSAC
Detection of Tumour Infiltrating Lymphocytes in CD3 and CD8 Stained Histopathological Images using a Two-Phase Deep CNN
Results: The empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0. 74.
