A frequency-aware unsupervised module for image region style classification and its fusion with existing in-lab weakly supervised virtual staining model.

Yuzhi Wang; Weixing Dai; Terence T. W. Wong

doi:10.1117/12.3001217

13 March 2024 A frequency-aware unsupervised module for image region style classification and its fusion with existing in-lab weakly supervised virtual staining model.

Yuzhi Wang, Weixing Dai, Terence T. W. Wong

Author Affiliations +

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC1285718 (2024) https://doi.org/10.1117/12.3001217
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

In this work, we propose an unsupervised module that combines wavelet-packet transform and k-means++ clustering to extract frequency features and classify patches of medical images. This module produces region labels for each patch-image, bypassing heavy computation and methodological labelling. Our WeCREST model, powered by this module, outperforms CycleGAN in terms of SSIM and PSNR, partly outperforms the supervised pix2pix, but underperforms compared to the state-of-the-art weakly supervised WeCREST. This improvement of the original WeCREST provides new insights into wavelet-based feature extraction and unsupervised region-style classification for medical images.

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Yuzhi Wang, Weixing Dai, and Terence T. W. Wong "A frequency-aware unsupervised module for image region style classification and its fusion with existing in-lab weakly supervised virtual staining model.", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC1285718 (13 March 2024); https://doi.org/10.1117/12.3001217

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