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Visual AI in Healthcare: From Pathology to Clinical Deployment

Event artwork for Visual AI in Healthcare: From Pathology to Clinical Deployment

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About this event

What to expect

Discover how AI-powered image analysis is reshaping modern healthcare at this comprehensive online meetup exploring Visual AI applications in medical imaging, pathology, and clinical deployment. Hosted by the Montreal AI, Machine Learning and Computer Vision Meetup, this event brings together leading researchers and practitioners to share cutting-edge developments at the intersection of computer vision, machine learning, and healthcare technology.

What You'll Learn

This session features three expert-led presentations covering the full spectrum of healthcare AI development—from foundational research through to real-world clinical deployment:

Representation Learning in Computational Pathology — Dr. Tolga Tasdizen from the University of Utah will explore weak- and semi-supervised learning methods that reduce dependence on dense annotations while improving accuracy in histopathology. Learn how adaptive stain separation, bag-label-aware contrastive pretraining, and distance-aware spatial modeling are advancing kidney and prostate cancer diagnosis through better tissue geometry understanding.

Efficient AI for Clinical Deployment — Md Mostafijur Rahman, Ph.D. candidate at UT Austin, addresses the critical gap between laboratory AI performance and real-world healthcare constraints. Discover lightweight deep learning architectures designed for deployment under strict compute, memory, and data limitations, plus training strategies that improve generalization to rare medical cases without inference overhead.

VIGIL: AI-Guided Rural Healthcare — Andrew Krikorian from the University of Michigan presents an AI-driven system that supports generalist clinicians through interactive, multimodal guidance. See how perception models, language understanding, and tool orchestration combine to assist with real-time ultrasound acquisition and interpretation in underserved rural settings.

Scaling with Synthetic Data and World Models — Daniel Gural explores how emerging world models generate privacy-compliant synthetic medical imaging data to overcome dataset scarcity. Learn practical workflows for curating and validating synthetic radiology scans and surgical simulations alongside real clinical data to accelerate diagnostic AI development responsibly.

Who Should Attend

This event is ideal for AI researchers, machine learning engineers, healthcare technologists, and computer vision practitioners interested in

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