4.6k citations · 6.3k across the 33 of their papers we have counts for
33 papers
MAIRA-2: Grounded Radiology Report Generation
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro +18
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solut…
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Anja Thieme, Abhijith Rajamohan, Benjamin Cooper +22
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious h…
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…
Exploring scalable medical image encoders beyond text supervision
Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor +12
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal sys…
RadEdit: stress-testing biomedical vision models via diffusion image editing
Fernando Pérez-García, Sam Bond-Taylor, Pedro P. Sanchez +11
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. Thi…
MAIRA-1: A specialised large multimodal model for radiology report generation
Stephanie L. Hyland, Shruthi Bannur, Kenza Bouzid +12
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s)…
Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu, Stephanie Hyland, Shruthi Bannur +16
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domai…
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
Charles Jones, Daniel C. Castro, Fabio De Sousa Ribeiro +3
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent. Despite considerable work dedicated to detect…
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query
Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkow +4
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomic…
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
Fangyu Liu, Qianchu Liu, Shruthi Bannur +9
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero…
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Shruthi Bannur, Stephanie Hyland, Qianchu Liu +13
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignm…
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur +9
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical re…
Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno +9
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment…
Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs
Shruthi Bannur, Ozan Oktay, Melanie Bernhardt +9
Chest radiography has been a recommended procedure for patient triaging and resource management in intensive care units (ICUs) throughout the COVID-19 pandemic. The machine learnin…
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
Matthew C. H. Lee, Ozan Oktay, Andreas Schuh +2
Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a compl…
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +12
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of p…
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Robert Robinson, Vanya V. Valindria, Wenjia Bai +19
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic proc…
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
Jo Schlemper, Ozan Oktay, Michiel Schaap +4
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs…
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
Wenjia Bai, Hideaki Suzuki, Chen Qin +4
Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct applic…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…
Real-time Prediction of Segmentation Quality
Robert Robinson, Ozan Oktay, Wenjia Bai +17
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…