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Found 10 papers, 3 papers with code
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Experiments show that X-Reasoner successfully transfers reasoning capabilities to both multimodal and out-of-domain settings, outperforming existing state-of-the-art models trained with in-domain and multimodal data across various general and medical benchmarks (Figure 1).
Boltzmann Attention Sampling for Image Analysis with Small Objects
Detecting and segmenting small objects, such as lung nodules and tumor lesions, remains a critical challenge in image analysis.
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0. 9 in most other domains.
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 #2 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.
