Vision Foundry: A System for Training Foundational Vision AI Models

2025/12/03 by Mahmut S. Gokmen, Gokmen, Mahmut S., Mitchell A Klusty +15
Computer Science · Medicine · #Domain Adaptation and Few-Shot Learning #COVID-19 diagnosis using AI #Medical Image Segmentation Techniques

paper · doi:10.48550/arxiv.2512.11837

Abstract

framework, abstracting distributed infrastructure complexities while implementing specialized strategies like Magnification-Aware Distillation (MAD) and Parameter-Efficient Fine-Tuning (PEFT). We validate the platform across domains, including neuropathology segmentation, lung cellularity estimation, and coronary calcium scoring. Our experiments demonstrate that models trained via Vision Foundry significantly outperform generic baselines in segmentation fidelity and regression accuracy, while exhibiting robust zero-shot generalization across imaging protocols. By bridging the gap between advanced representation learning and practical application, Vision Foundry enables domain experts to develop state-of-the-art clinical AI tools with minimal annotation overhead, shifting focus from engineering optimization to clinical discovery.

Citations

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Related

• Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
• Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis
• AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models
• Prompt Triage: Structured Optimization Enhances Vision-Language Model Performance on Medical Imaging Benchmarks
• Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining