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13 March 2024 Attention-based weakly-supervised deep learning for the identification and localization of drug fingerprints based on label-free hyperspectral CARS microscopy
Jindou Shi, Kajari Bera, Prabuddha Mukherjee, Aneesh Alex, Eric J. Chaney, Bradley Spencer-Dene, Jan Majer, Marina Marjanovic, Darold R. Spillman Jr., Steve R. Hood, Stephen A. Boppart
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Proceedings Volume PC12821, Visualizing and Quantifying Drug Distribution in Tissue VIII; PC1282106 (2024) https://doi.org/10.1117/12.3001269
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Understanding drug fingerprints in complex biological samples is essential for drug development. We demonstrate a deep learning-assisted hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) imaging approach for identifying drug fingerprints at single-cell resolution. The attention-based deep neural network, Hyperspectral Attention Net (HAN), highlights informative spatial and spectral regions in a weakly supervised manner. Using this approach, drug fingerprints of a hepatitis B virus therapy in murine liver tissues was investigated. Higher classification accuracy was observed with increasing drug dosage, reaching an average AUC of 0.942. Results demonstrate the potential for label-free profiling and localization of drug fingerprints in complex biological samples.
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Jindou Shi, Kajari Bera, Prabuddha Mukherjee, Aneesh Alex, Eric J. Chaney, Bradley Spencer-Dene, Jan Majer, Marina Marjanovic, Darold R. Spillman Jr., Steve R. Hood, and Stephen A. Boppart "Attention-based weakly-supervised deep learning for the identification and localization of drug fingerprints based on label-free hyperspectral CARS microscopy", Proc. SPIE PC12821, Visualizing and Quantifying Drug Distribution in Tissue VIII, PC1282106 (13 March 2024); https://doi.org/10.1117/12.3001269
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KEYWORDS
Microscopy
Deep learning
Biological samples
Complex systems
Nervous system
Nondestructive evaluation
Optical imaging
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