Search Results for author:
Found 9 papers, 4 papers with code
BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature
The development of vision-language models (VLMs) is driven by large-scale and diverse multimodal datasets.
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation.
Time-to-Event Pretraining for 3D Medical Imaging
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk.
μ-Bench: A Vision-Language Benchmark for Microscopy Understanding
Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Zero-Shot Clinical Trial Patient Matching with LLMs
First, we design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria (also specified as free text).
Open World Object Detection in the Era of Foundation Models
We exploit the inherent connection between classes in application-driven datasets and introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
