5 papers
Comprehensive language-image pre-training for 3D medical image understanding
Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao +14
In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abn…
Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting
Harshita Sharma, Maxwell C. Reynolds, Valentina Salvatelli +26
AI-assisted report generation offers the opportunity to reduce radiologists' workload stemming from expanded screening guidelines, complex cases and workforce shortages, while main…
Data Scaling Laws for Radiology Foundation Models
Maximilian Ilse, Harshita Sharma, Anton Schwaighofer +12
Foundation vision encoders such as CLIP and DINOv2, trained on web-scale data, exhibit strong transfer performance across tasks and datasets. However, medical imaging foundation mo…
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…
Exploring scalable medical image encoders beyond text supervision
Fernando Pérez-GarcÃa, 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…