SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation

• URL: http://arxiv.org/abs/2201.08582v4
• Date: Sat, 30 Sep 2023 07:01:56 GMT
• Title: SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation
• Authors: Quan-Dung Pham (1), Hai Nguyen-Truong (1, 2 and 3), Nam Nguyen Phuong (1) and Khoa N. A. Nguyen (1, 2 and 3) ((1) VinBrain JSC., Vietnam, (2) University of Science, Ho Chi Minh City, Vietnam, (3) Vietnam National University, Ho Chi Minh City, Vietnam)
• Abstract summary: A novel network named SegTransVAE is proposed in this paper. SegTransVAE is built upon encoder-decoder architecture, exploiting transformer with the variational autoencoder (VAE) branch to the network. Evaluation on various recently introduced datasets shows that SegTransVAE outperforms previous methods in Dice Score and $95%$-Haudorff Distance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current research on deep learning for medical image segmentation exposes their limitations in learning either global semantic information or local contextual information. To tackle these issues, a novel network named SegTransVAE is proposed in this paper. SegTransVAE is built upon encoder-decoder architecture, exploiting transformer with the variational autoencoder (VAE) branch to the network to reconstruct the input images jointly with segmentation. To the best of our knowledge, this is the first method combining the success of CNN, transformer, and VAE. Evaluation on various recently introduced datasets shows that SegTransVAE outperforms previous methods in Dice Score and $95\%$-Haudorff Distance while having comparable inference time to a simple CNN-based architecture network. The source code is available at: https://github.com/itruonghai/SegTransVAE.

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