Project MAIRA
Multimodal AI for Radiology Applications
News & features
Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment
| Max Ilse, Daniel Coelho de Castro, and Javier Alvarez-Valle
RadEdit stress-tests biomedical vision models by simulating dataset shifts through precise image editing. It uses diffusion models to create realistic, synthetic datasets, helping to identify model weaknesses and evaluate robustness.
Research Focus: Week of June 24, 2024
In this issue: RENC makes 5G vRAN servers more energy efficient; CoExplorer uses AI to keep video meetings on track; Automatic bug detection in LLM-powered text-based games; MAIRA-2: Grounded radiology report generation.
Research at Microsoft 2023: A year of groundbreaking AI advances and discoveries
AI saw unparalleled growth in 2023, reaching millions daily. This progress owes much to the extensive work of Microsoft researchers and collaborators. In this review, learn about the advances in 2023, which set the stage for further progress in 2024.
GPT-4’s potential in shaping the future of radiology
| Javier Alvarez-Valle and Matthew Lungren
This research paper is being presented at the 2023 Conference on Empirical Methods in Natural Language Processing (opens in new tab) (EMNLP 2023), the premier conference on natural language processing and artificial intelligence. In recent years, AI has been increasingly…
Accounting for past imaging studies: Enhancing radiology AI and reporting
| Ozan Oktay, Javier Alvarez-Valle, and Matthew Lungren
The use of self-supervision from image-text pairs has been a key enabler in the development of scalable and flexible vision-language AI models in not only general domains but also in biomedical domains such as radiology. The goal in the radiology…