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12 November 2024 MSF-TransUNet: transformer multiscale fusion on U-Net for gastric cancer pathological image segmentation
Bing Bai, Xiaoqi Zhang
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133951S (2024) https://doi.org/10.1117/12.3048552
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
To address the challenges in gastric cancer pathological images, such as varying sizes and shapes of lesion regions as well as blurry boundaries, we propose an enhanced U-Net architecture segmentation algorithm based on an affine crossattention mechanism. Specifically, we introduce affine transformation modules into the up-sampling and down-sampling stages of the U-Net, replacing adjacent convolutional blocks to better capture variations in shape and size. Additionally, a cross-attention module is incorporated in the bridging phase to enhance feature utilization and mitigate mis-segmentation of healthy tissues. In contrast to the conventional U-Net, our algorithm demonstrates notable enhancements in terms of 8.21%, 6.87%, and 5.57% in Dice coefficient, Intersection over Union (IoU), and Accuracy (ACC), respectively. The effectiveness of our introduced modules is reinforced through ablation investigations. The segmentation performance of lesion regions in gastric cancer pathological images is augmented by the proposed algorithm, as shown by the experimental results, effectively reducing the false-positive rate in image diagnosis
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Bing Bai and Xiaoqi Zhang "MSF-TransUNet: transformer multiscale fusion on U-Net for gastric cancer pathological image segmentation", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133951S (12 November 2024); https://doi.org/10.1117/12.3048552
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KEYWORDS
Image segmentation
Semantics
Transformers
Cancer
Education and training
Medical imaging
Image processing
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