GedankenNet: self-supervised learning of holographic imaging enabled by physics consistency

Luzhe Huang; Hanlong Chen; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.3027298

4 October 2024 GedankenNet: self-supervised learning of holographic imaging enabled by physics consistency

Luzhe Huang, Hanlong Chen, Tairan Liu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311814 (2024) https://doi.org/10.1117/12.3027298
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We present GedankenNet, a self-supervised learning framework designed to eliminate reliance on experimental training data for holographic image reconstruction and phase retrieval. Analogous to thought (Gedanken) experiments in physics, the training of GedankenNet is guided by the consistency of physical laws governing holography without any experimental data or prior knowledge regarding the samples. When blindly tested on experimental data of various biological samples, GedankenNet performed very well and outperformed existing supervised models on external generalization. We further showed the robustness of GedankenNet to perturbations in the imaging hardware, including unknown changes in the imaging distance, pixel size and illumination wavelength.

Conference Presentation

Citation Download Citation

Luzhe Huang, Hanlong Chen, Tairan Liu, and Aydogan Ozcan "GedankenNet: self-supervised learning of holographic imaging enabled by physics consistency", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311814 (4 October 2024); https://doi.org/10.1117/12.3027298

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