Project MAIRA
Multimodal AI for Radiology Applications
新闻与深度文章
| Max Ilse, Daniel Coelho de Castro, 和 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.
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.
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.
| Javier Alvarez-Valle 和 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…
| Ozan Oktay, Javier Alvarez-Valle, 和 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…