  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.
