Project MAIRA
Multimodal AI for Radiology Applications
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RAD-DINO model
novembre 2024
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. Bond-Taylor, et al., 2024).
MAIRA-2 model
novembre 2024
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., 2024). MAIRA-2 has been built…
RadFact: An LLM-based Evaluation Metric for AI-generated Radiology Reporting
novembre 2024
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 metrics, capturing aspects of precision…
HI-ML Multimodal Toolbox
mai 2023
HI-ML toolbox for deep learning for medical imaging and Azure integration. The Microsoft Health Intelligence Machine Learning Toolbox aims at providing low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It helps to simplify and streamline…