Biomedical Imaging
Addressing the challenges of speed, quantification, and cost
新闻与深度文章
| Peter Lee, Dr. Umair Shah, 和 Dr. Gianrico Farrugia
Former Washington State Secretary of Health Dr. Umair Shah and Mayo Clinic CEO Dr. Gianrico Farrugia explore how healthcare leaders are approaching AI when it comes to public health, care delivery, the healthcare-research connection, and the patient experience.
| Daniel Coelho de Castro 和 Javier Alvarez-Valle
The world’s first multimodal, bilingual radiology dataset could reshape the way radiologists and AI systems make sense of X-rays. PadChest-GR, developed by the University of Alicante with Microsoft Research, has the potential to advance research across the field for years…
| 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.
| Anton Schwaighofer 和 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…
| 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…
| Gretchen Huizinga, Javier Alvarez-Valle, 和 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.
| 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…
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.