Our organization, Microsoft Health Futures, is an interdisciplinary group of researchers, data scientists, computational biologists, bioinformaticians, engineers, physicians, and user experience researchers who are working to accelerate biomedical discovery. We apply the design thinking process…
RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. RAD-DINO is described in detail in RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S.…
MAIRA-2 is a multimodal transformer designed for the generation of grounded or non-grounded radiology reports from chest X-rays. It is described in more detail in MAIRA-2: Grounded Radiology Report Generation (S. Bannur, K. Bouzid et al.,…
RadFact is a framework for the evaluation of model-generated radiology reports given a ground-truth report, with or without grounding. Leveraging the logical inference capabilities of large language models, RadFact is not a single number but a suite of…
Bioinformatics, biomedical natural language processing (NLP), and generative AI can play key roles in this transformation by discerning knowledge from data and separating signal from noise. We are looking for a Research Intern with experience…
Image analysis is fundamental for clinical diagnostics and biomedical discovery. In this video, we introduce BiomedParse, a biomedical foundation model for holistic image analysis that can jointly conduct recognition, detection, and segmentation for 64 major…
BiomedParse reimagines medical image analysis, integrating advanced AI to capture complex insights across imaging types—a step forward for diagnostics and precision medicine.
