Search Results for author:
Found 7 papers, 1 papers with code
BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once
On object recognition, which aims to identify all objects in a given image along with their semantic types, we showed that BiomedParse can simultaneously segment and label all biomedical objects in an image (all at once).
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Frontier general-domain models such as GPT-4V still have significant performance gaps in multimodal biomedical applications.
Foundation Models for Biomedical Image Segmentation: A Survey
This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.
Pareto Optimal Learning for Estimating Large Language Model Errors
We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information.
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs.
Ranked #3 on
Medical Visual Question Answering
on SLAKE-English
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Results: We conduct an extensive study on 135, 107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states.
