Hardware domain adaptation for biomedical imaging

Amey Chaware; Kanghyun Kim; Clare Cook; Carolyn Glass; Roarke Horstmeyer

doi:10.1117/12.3000388

13 March 2024 Hardware domain adaptation for biomedical imaging

Amey Chaware, Kanghyun Kim, Clare Cook, Carolyn Glass, Roarke Horstmeyer

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC128570S (2024) https://doi.org/10.1117/12.3000388
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We present a scheme termed Hardware Domain Adaptation that transforms the visual appearance of biomedical images to match that of a given optical system. This allows us to exploit large publicly available datasets for the training of custom machine learning algorithms for inference on data sets captured by a different imaging hardware for the same task. Moreover, this method allows us to train models for lower-quality image datasets that are difficult or impossible to annotate manually. We demonstrate the efficacy of this method by using publicly available data to train an algorithm to identify and count white blood cells in images obtained on our custom hardware.

Conference Presentation

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

Citation Download Citation

Amey Chaware, Kanghyun Kim, Clare Cook, Carolyn Glass, and Roarke Horstmeyer "Hardware domain adaptation for biomedical imaging", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC128570S (13 March 2024); https://doi.org/10.1117/12.3000388

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