4 papers
Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images
Mahmut S. Gokmen, Mitchell A. Klusty, Peter T. Nelson +8
Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. Thi…
Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching
Caroline N. Leach, Mitchell A. Klusty, Samuel E. Armstrong +6
Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artifi…
Vision Foundry: A System for Training Foundational Vision AI Models
Mahmut S. Gokmen, Mitchell A. Klusty, Evan W. Damron +6
Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce Vision Foundry, a code…
Semantic Nutrition Estimation: Predicting Food Healthfulness from Text Descriptions
Dayne R. Freudenberg, Daniel G. Haughian, Mitchell A. Klusty +8
Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descripti…