231 citations · 319 across the 8 of their papers we have counts for
8 papers
Foundation Models for Biomedical Image Segmentation: A Survey
Ho Hin Lee, Yu Gu, Theodore Zhao +9
Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for genera…
Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu, Stephanie Hyland, Shruthi Bannur +16
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domai…
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
Yu Gu, Jianwei Yang, Naoto Usuyama +5
Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be app…
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Cliff Wong, Sheng Zhang, Yu Gu +8
Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this pa…
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
Yu Gu, Sheng Zhang, Naoto Usuyama +8
Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs…
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Chunyuan Li, Cliff Wong, Sheng Zhang +6
Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversation…