Fusing Brilliance: Evaluating the Encoder-Decoder Hybrids With CNN and Swin Transformer for Medical Segmentation

U-Net has become a standard model for medical image segmentation, alleviating the challenges posed by the costly acquisition and labeling of medical data. The convolutional layer, a fundamental component of U-Net, is renowned for its ability to incorporate inductive bias and efficiently extract loca...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Seunghyuk Lee, Songkuk Kim
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Subjects:	Deep neural network medical image segmentation Swin Transformer U-Net
Online Access:	https://ieeexplore.ieee.org/document/10507838/