Machine Learning for Multimodal Healthcare Data

This new workshop will bring together interdisciplinary scientists and practitioners working at the intersections of machine learning (ML) to medicine, pathology and biology, for presenting new methods and solutions for healthcare challenges across the full range of multimodal, and often highly heterogeneous and complex patient data, to the wider ICML community. Topics of interest include, but are not limited to: Multimodal fusion and learning in medical imaging, digital pathology, computational biology, genetics, electronic healthcare records; Multimodal biomarkers for early prediction of disease onset, therapeutic response or disease recurrence; Benchmarking, domain shifts, and generalization of ML in multimodal healthcare data; ML for dealing with inherent sparsity, incompleteness and complexity of multimodal healthcare data; ML for ensuring fairness and reducing bias in healthcare applications; ML for privacy preservation in healthcare data; Co-creation and human-in-the-loop for ML in healthcare.