activity
20202025
most citedMaking the Most of Text Semantics to Improve Biomedical Vision--Language Processing

264 citations · 613 across the 25 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CV2024★ 1 cited

MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models

Harshita Sharma, Valentina Salvatelli, Shaury Srivastav +13

There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information…

cs.AI2024★ 19 cited

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…

eess.IV2024★ 4 cited

MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging

Noel C. F. Codella, Ying Jin, Shrey Jain +28

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…

cs.CL2024★ 16 cited

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★ 1 cited

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.HC2024★ 78 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…

cs.CV2024★ 69 cited

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…