Condensed UNETR: a lightweight medical image segmenter

Zhenyu Lu; Jiaming Liang; Danmin Huang; Weihao Zhang; Teng Huang; Yan Pang

doi:10.1117/12.3038469

22 October 2024 Condensed UNETR: a lightweight medical image segmenter

Zhenyu Lu, Jiaming Liang, Danmin Huang, Weihao Zhang, Teng Huang, Yan Pang

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132741N (2024) https://doi.org/10.1117/12.3038469
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

Hybrid transformer-based segmentation methods have proven to be highly effective in analyzing medical images. However, they often demand significant computational resources for training and inference, which can be challenging for resource-scarce medical settings. In response, a novel framework named Condensed UNETR is introduced, which strikes a balance between precision and efficiency by combining the strengths of convolutional neural networks and transformers. The Condensed UNETR Block, a key element of this approach, facilitates efficient information flow through a self-attention mechanism decomposition and streamlined representation merging. The framework also incorporates the throughput metric as a measure of efficiency to monitor the model's resource usage. Experiments have shown that Condensed UNETR surpasses leading models in accuracy, size, and efficiency on devices with limited resources.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenyu Lu, Jiaming Liang, Danmin Huang, Weihao Zhang, Teng Huang, and Yan Pang "Condensed UNETR: a lightweight medical image segmenter", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132741N (22 October 2024); https://doi.org/10.1117/12.3038469

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