755 citations · 874 across the 7 of their papers we have counts for
7 papers
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Dave Van Veen, Cara Van Uden, Louis Blankemeier +16
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large langua…
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification
Cara Van Uden, Christian Bluethgen, Maayane Attias +5
Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning a…
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
Dave Van Veen, Cara Van Uden, Maayane Attias +9
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain ad…
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck +7
Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in generating high-quality images. Medical imaging data is fundamentally dif…
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen +3
Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying po…
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz +1
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches. Although such mod…