Biomedical Imaging
Addressing the challenges of speed, quantification, and cost
Nouvelles et reportages
Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment
| Max Ilse, Daniel Coelho de Castro, et 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.
Scaling early detection of esophageal cancer with AI
| Anton Schwaighofer et Javier Alvarez-Valle
Microsoft Research and Cyted have collaborated to build novel AI models (opens in new tab) to scale the early detection of esophageal cancer. The AI-supported methods demonstrated the same diagnostic performance as the existing manual workflow, potentially reducing the pathologist’s…
GPT-4’s potential in shaping the future of radiology
| Javier Alvarez-Valle et 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…
Collaborators: Project InnerEye with Javier Alvarez and Raj Jena
| Gretchen Huizinga, Javier Alvarez-Valle, et Raj Jena
Microsoft Health Futures’ Javier Alvarez & oncologist Raj Jena have been collaborating for years on AI-assisted medical imaging. Today, their work is seeing real-world impact, helping doctors accelerate cancer patients’ access to treatment.
Accounting for past imaging studies: Enhancing radiology AI and reporting
| Ozan Oktay, Javier Alvarez-Valle, et 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…
Research Focus: Week of May 8, 2023
In this issue: Microsoft researchers win four more awards; AutoRXN automates calculations of molecular systems; LLM accelerator losslessly improves the efficiency of autoregressive decoding; a frequency domain approach to predict power system transients.
Research Focus: Week of October 24, 2022
Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Microsoft is thrilled to announce the 2022 Microsoft Research Global PhD Fellows…
Dans l’actualité | Stanford University HAI
The Open-Source Movement Comes to Medical Datasets
In a move to democratize research on artificial intelligence and medicine, Stanford’s Center for Artificial Intelligence in Medicine and Imaging (AIMI) is dramatically expanding what is already the world’s largest free repository of AI-ready annotated medical imaging datasets.