Advances in run-time strategies for next-generation foundation models
Discover the most effective run-time strategies on the OpenAI o1-preview model, improving accuracy in medical language tasks.
RAD-DINO model
RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2 (opens in new tab). RAD-DINO is described in detail in RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F.…
MAIRA-2 model
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: An LLM-based Evaluation Metric for AI-generated Radiology Reporting
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…
RadPhi-3: Small Language Models for Radiology
Introducing BiomedParse, a groundbreaking foundation model for biomedical image analysis
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: A foundation model for smarter, all-in-one biomedical image analysis
BiomedParse reimagines medical image analysis, integrating advanced AI to capture complex insights across imaging types—a step forward for diagnostics and precision medicine.
Abstracts: November 14, 2024
The efficient simulation of molecules has the potential to change how the world understands biological systems and designs new drugs and biomaterials. Tong Wang discusses AI2BMD, an AI-based system designed to simulate large biomolecules with…