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

arXiv:2403.17992v1 Announce Type: cross
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. …

abstract advances artificial artificial intelligence arxiv cancer cells cs.ai cs.lg data detection eess.iv eess.sp experiment however identify information intelligence microscopy networks neural networks q-bio.qm show technical technology type