Overview

Machine learning (ML) has achieved or even exceeded human performance in many healthcare tasks, owing to the fast development of ML techniques and the growing scale of medical data. However, ML techniques are still far from being widely applied in practice. Real-world scenarios are far more complex, and ML is often faced with challenges in its trustworthiness such as lack of explainability, generalization, fairness, privacy, etc. Improving the credibility of machine learning is hence of great importance to enhance the trust and confidence of doctors and patients in using the related techniques. We aim to bring together researchers from interdisciplinary fields, including but not limited to machine learning, clinical research, and medical imaging, etc., to provide different perspectives on how to develop trustworthy ML algorithms to accelerate the landing of ML in healthcare.

Scope and Topics

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. 

The goal of this workshop is to bring together expertise from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability. The challenges to ML come from diverse perspectives in practice, and it is therefore of great importance to establish such an interdisciplinary platform to encourage sharing and discussion of ideas, implementation, data, labelling, benchmarks, experience, etc, and jointly advance the frontiers of trustworthy ML in healthcare.

Tentatively, the workshop will be hosted virtually. 

Important Dates

Paper Submission Deadline: February 10, 2023 

Extended Paper Submission Deadline: February 20, 2023 (11:59 PM UTC-0)

Decision Notification Date: March 3, 2023 March 7, 2023

Workshop Date:  May 4, 2023

An official workshop proceeding will be published in the LNCS (Lecture Notes in Computer Science) series of Springer. 

Camera-ready Deadline:  May 20, 2023

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