Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration

• URL: http://arxiv.org/abs/2403.17992v1
• Date: Tue, 26 Mar 2024 12:20:10 GMT
• Title: Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration
• Authors: Yijie Zheng, Rafael Fuentes-Dominguez, Matt Clark, George S. D. Gordon, Fernando Perez-Cota,
• Abstract summary: We present a conditional neural network framework to simultaneously achieve inter-batch calibration. We validate our approach by training and validating on different experimental batches. We extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state.
• Score: 39.759100498329275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in artificial intelligence (AI) show great potential in revealing underlying information from phonon microscopy (high-frequency ultrasound) data to identify cancerous cells. However, this technology suffers from the 'batch effect' that comes from unavoidable technical variations between each experiment, creating confounding variables that the AI model may inadvertently learn. We therefore present a multi-task conditional neural network framework to simultaneously achieve inter-batch calibration, by removing confounding variables, and accurate cell classification of time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Classification can be performed in 0.5 seconds with only simple prior batch information required for multiple batch corrections. Further, we extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state including sound velocity, sound attenuation and cell-adhesion to substrate.

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