755 citations · 1.1k across the 14 of their papers we have counts for
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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…