activity
20162025
most citedReal-valued (Medical) Time Series Generation with Recurrent Conditional GANs

268 citations · 381 across the 11 of their papers we have counts for

collaborators

19 papers

cs.LG2025

Insights into a radiology-specialised multimodal large language model with sparse autoencoders

Kenza Bouzid, Shruthi Bannur, Felix Meissen +4

Interpretability can improve the safety, transparency and trust of AI models, which is especially important in healthcare applications where decisions often carry significant conse…

cs.AI2024

PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation

Daniel C. Castro, Aurelia Bustos, Shruthi Bannur +11

Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localis…

cs.CV20242 cited

An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation

Ahmed Abdulaal, Hugo Fry, Nina Montaña-Brown +5

Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer…

cs.CL2024

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…

cs.HC2024

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…

cs.HC202478 cited

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…