Physics-informed and interpretable machine learning for QPM: instrumentation, algorithms to applications

Dushan N. Wadduwage

doi:10.1117/12.3005078

13 March 2024 Physics-informed and interpretable machine learning for QPM: instrumentation, algorithms to applications

Dushan N. Wadduwage

Proceedings Volume PC12852, Quantitative Phase Imaging X; PC128520T (2024) https://doi.org/10.1117/12.3005078
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

With applications in pathogen screening, cancer cell classification, and digital pathology, quantitative phase microscopy (QPM) quantifies optical phase differences to yield information about the morphology, dry mass, and dynamics of biological specimens. Being a label-free physical measurement, QPM is well suited to be integrated with modern physics-based machine-learning approaches. To this end, we present end-to-end differentiable physical and analytical neural models to build optimal QPM solutions for target applications. We further discuss the interpretability of our models from a statical viewpoint to critically evaluate their performance beyond simple accuracy tests.

Conference Presentation

Citation Download Citation

Dushan N. Wadduwage "Physics-informed and interpretable machine learning for QPM: instrumentation, algorithms to applications", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520T (13 March 2024); https://doi.org/10.1117/12.3005078

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