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Visual AI in Healthcare: Pathology, Deployment & Clinical Systems

Event artwork for Visual AI in Healthcare: Pathology, Deployment & Clinical Systems

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Hugging Face
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What to expect

Explore the cutting edge of Visual AI in Healthcare at this expert-led online meetup featuring speakers from leading research institutions tackling the most challenging problems in medical AI. This comprehensive 3-hour session brings together innovators working at the intersection of computer vision, machine learning, and clinical deployment to share breakthrough approaches in computational pathology, resource-efficient AI systems, and embodied healthcare agents.

Featured Talks

Representation Learning Under Weak Supervision in Computational Pathology — Dr. Tolga Tasdizen, Professor at the University of Utah, presents cutting-edge methods for learning robust representations from whole-slide pathology images with minimal annotation requirements. His work addresses critical challenges including domain shift, geometric tissue modeling, and the scarcity of labeled histopathology data. Expect insights on adaptive stain separation, bag-label-aware contrastive learning, and distance-aware spatial modeling with proven results in kidney and prostate cancer diagnosis.

Efficient and Reliable AI for Real-World Healthcare Deployment — Md Mostafijur Rahman, Ph.D. candidate at UT Austin, tackles the deployment gap between laboratory AI and clinical reality. His research focuses on building lightweight deep learning architectures that maintain accuracy under strict resource constraints while handling rare, long-tailed clinical findings. Drawing from research internships at GE Healthcare, NIH, and Bosch Research, he'll share practical approaches to training-time generative supervision and real-time surgical perception systems.

VIGIL: Vectors of Intelligent Guidance in Long-Reach Rural Healthcare — Andrew Krikorian, University of Michigan Ph.D. student and member of the Corso Group, introduces an AI-driven system designed to support generalist clinicians in resource-limited settings. This systems-level presentation covers the architecture of multimodal AI agents that provide real-time guidance for ultrasound acquisition and interpretation, addressing critical challenges in tool orchestration, latency, and cross-component robustness for the ARPA-H PARADIGM program.

Scaling Healthcare AI with Synthetic Data and World Models — Learn how emerging world models are generating high-fidelity synthetic medical imaging da

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