Biomedical Imaging
Addressing the challenges of speed, quantification, and cost
Downloads
RAD-DINO model
November 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
November 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
November 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…
Gadgetron Azure
September 2021
This repository contains a setup for running a distributed deployment of the Gadgetron in a Kubernetes cluster. The setup has been developed for and tested with Azure Kubernetes Service (AKS) but should work on other Kubernetes deployments too.
fastMRI+
September 2021
The purpose of this project is to release a set of clinically relevant labels for an existing, publicly available dataset (https://fastmri.org). With permission from the fastMRI team at NYU, we have engaged third party labelers (radiologist) to place bounding boxes…
InnerEye Inference API
May 2021
InnerEye-Inference is a AppService webapp in python to run inference on medical imaging models trained with the InnerEye-DeepLearning toolkit. You can also integrate this with DICOM using the InnerEye-EdgeGateway.
InnerEye-DICOM-RT
April 2021
InnerEye-DICOM-RT contains tools to convert medical datasets in NIFTI format to DICOM-RT. Datasets converted using this tool can be consumed directly by InnerEye-DeepLearning. Most of the work is done by a .NET Core 2.1 project in RTConvert, written in C#.…
InnerEye-CreateDataset
September 2020
InnerEye-CreateDataset contains tools to convert medical datasets in DICOM-RT format to NIFTI. Datasets converted using this tool can be consumed directly by InnerEye-DeepLearning.
InnerEye – Deep Learning
September 2020
This is a deep learning toolbox to train models on medical images (or more generally, 3D images). It integrates seamlessly with cloud computing in Azure.