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Found 22 papers, 7 papers with code
Exploring Scaling Laws for EHR Foundation Models
The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through systematic increases in model size, dataset volume, and compute.
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.
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks.
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.
When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology
This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #2 on
Question Answering
on MedQA
(using extra training data)
Exploring the Boundaries of GPT-4 in Radiology
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Clinical trial matching is a key process in health delivery and discovery.
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.
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
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
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing.
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.
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
Modular Self-Supervision for Document-Level Relation Extraction
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Ranked #2 on
Participant Intervention Comparison Outcome Extraction
on EBM-NLP
(using extra training data)
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification
Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e. g. white round pills), which increases the risk of medication errors.
Ranked #1 on
Pill Classification (Both Sides)
on ePillID
(using extra training data)
Few-Shot Image Classification
Fine-Grained Image Recognition
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