Workshop Series on Reliable and Interpretable Deep Learning
Challenges, Theories and Applications in Biomedical Informatics
About WS-RIDL 2026
WS-RIDL 2026 gathers researchers at the intersection of AI and biomedical informatics around a shared concern: deep learning systems are increasingly powerful, but deploying them in clinical and scientific settings demands something more. The workshop explores the theory and practice of building models that are robust, transparent, and accountable.
By uniting world-leading scholars from Hong Kong, Mainland China, and the broader international community, WS-RIDL 2026 provides a forum for exchanging ideas on building deep learning systems that are not only powerful but also accountable, auditable, and safe for deployment in biomedical and clinical settings.
Workshop Highlights
Keynote Presentations
See what it takes for powerful AI to become a credible scientific tool: lower computational cost, more dependable reasoning, and explanations researchers can inspect and challenge.
View Keynotes →Invited Talks
Learn how researchers move beyond headline accuracy when biomedical data are limited, noisy, or multimodal—and how they test whether models remain robust, interpretable, and useful in practice.
View Invited Talks →Hands-on Tutorials
Gain hands-on experience turning modern AI into workable biomedical research pipelines, with practical guidance on structuring analyses, checking model behavior, and producing reproducible results.
Explore Hands-on Tutorials →Organized By
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