The CoSyn tool leverages synthetic data to help open-source AI models excel at understanding complex, text-rich images such as medical diagrams.
Key Details
- 1Penn Engineering and the Allen Institute for AI developed CoSyn to generate scientific charts and diagrams as training data for open-source vision-language models.
- 2CoSyn-400K includes over 400,000 synthetic images and 2.7 million sets of instructions, spanning scientific charts, chemical structures, and more.
- 3CoSyn-trained models outperformed proprietary systems, including GPT-4V and Gemini 1.5 Flash, on seven benchmarks.
- 4A small synthetic dataset (7,000 images) allowed their model to beat others trained on millions of real images for the NutritionQA benchmark.
- 5The approach eliminates copyright risks and supports wide, open-source access.
Why It Matters
Synthetic data generation like CoSyn can democratize advanced image understanding for medical and radiology AI, improving model accuracy while addressing data scarcity and copyright barriers. This supports innovation in clinical decision support and scientific research for radiology professionals.
Source
EurekAlert
Related News
•EurekAlert
Framework Guides Digital Pathology and AI Integration for Labs
A new lifecycle framework outlines practical steps for sustainable digital pathology and AI program implementation in clinical labs.
•EurekAlert
Recent AI Advances in Digital Breast Pathology: Models, Explainability, and Applications
AI is rapidly transforming breast pathology by improving diagnostic accuracy, workflow efficiency, and precision medicine through advances in deep learning and multimodal models.
•EurekAlert
HKUST Releases Misalignment-Resistant GenAI for Virtual Pathology Staining
HKUST researchers created a generative AI tool that enables high-fidelity virtual staining in histopathology even with imperfectly aligned training image pairs.