Biomedical Imaging
Addressing the challenges of speed, quantification, and cost
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RAD-DINO model
11월 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
11월 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
11월 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…
PadChest-GR dataset
11월 2024
PadChest-GR is a manually annotated, bilingual chest X-ray dataset designed to train and evaluate models for grounded radiology report generation. It includes bounding boxes and comprehensive annotations of all clinically relevant findings.
Gadgetron Azure
9월 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+
9월 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
5월 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
4월 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
9월 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.