755 citations · 1.1k across the 14 of their papers we have counts for
14 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…
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper +2
Vision-language models (VLMs), such as CLIP and ALIGN, are generally trained on datasets consisting of image-caption pairs obtained from the web. However, real-world multimodal dat…
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…
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems
Cara Van Uden, Jeremy Irvin, Mars Huang +4
Self-supervised learning (SSL) enables label efficient training for machine learning models. This is essential for domains such as medical imaging, where labels are costly and time…
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…
Almanac: Retrieval-Augmented Language Models for Clinical Medicine
Cyril Zakka, Akash Chaurasia, Rohan Shad +11
Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-a…
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays
Rogier van der Sluijs, Nandita Bhaskhar, Daniel Rubin +2
Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging ha…
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…
Toward expanding the scope of radiology report summarization to multiple anatomies and modalities
Zhihong Chen, Maya Varma, Xiang Wan +2
Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression sectio…
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…
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…
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Saahil Jain, Ashwin Agrawal, Adriel Saporta +9
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In…
Predicting post-operative right ventricular failure using video-based deep learning
Rohan Shad, Nicolas Quach, Robyn Fong +17
Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the…
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
Yuhao Zhang, Yuhui Zhang, Peng Qi +2
We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabil…