Trustworthy Machine Learning for Healthcare Workshop

Scope and Topics

We invite submissions of long and short research papers. We invite both types of papers for oral and poster presentations. We also welcome perspectives and poster papers to discuss major challenges and future trends. 

Interested topics will include, but not be limited to:

• Generalization to out-of-distribution samples.

• Explainability of machine learning models in healthcare.

• Reasoning, intervening, or causal inference.

• Debiasing ML models from learning shortcuts.

• Fair ML for healthcare.

• Uncertainty estimation of ML models and medical data.

• Privacy-preserving ML for medical data.

• Learning informative and discriminative features under weak annotations.

• Human-machine cooperation (human-in-the-loop, active learning, etc.) in healthcare, such as medical image analysis.

• Multi-modal fusion and learning, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, pathology, genetics, electronical healthcare records, etc.

• Benchmarks that quantify the trustworthiness of ML models in medical imaging tasks.