Donnat, C., Miolane, N., Bunbury, F., Kreindler, J.
NeurIPS Workshop on Machine Learning for Health.
1st Prize: C3.ai Grand Covid Challenge (100,000$).
Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.). While this field has typically focused on fully harvesting the signal provided by a single (and generally extremely reliable) modality, fewer efforts have utilized imprecise data lacking reliable ground truth labels. We devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous, and potentially unreliable, data types. We showcase the practicality of this approach by deploying it on a real COVID-19 immunity study.
