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Found 20 papers, 10 papers with code
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
While multimodal large language models (MLLMs) have shown advances in medical visual question answering (VQA), their ability to learn multimodal tasks from context is largely unknown.
Automated Structured Radiology Report Generation
To address this, we introduce Structured Radiology Report Generation (SRRG), a new task that reformulates free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting.
MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
In this work, we address the challenge of downsizing medical images in order to improve downstream computational efficiency while preserving clinically-relevant features.
Best Practices for Large Language Models in Radiology
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights.
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
We explored methods like using a proxy model and using radiologist feedback to improve the quality of synthetic data.
Foundation Models in Radiology: What, How, When, Why and Why Not
Given the potentially transformative impact that foundation models can have on the field of radiology, this review aims to establish a standardized terminology concerning foundation models, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies.
Preference Fine-Tuning for Factuality in Chest X-Ray Interpretation Models Without Human Feedback
The challenge in radiology lies in the prohibitive cost of obtaining radiologist feedback.
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision.
GREEN: Generative Radiology Report Evaluation and Error Notation
Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images.
Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
By combining CT-CLIP's vision encoder with a pretrained large language model, we create CT-CHAT, a vision-language foundational chat model for 3D chest CT volumes.
A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language Models
Furthermore, we introduce a benchmark of the summarization performance of two general-purpose LLMs and three healthcare-adapted LLMs.
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality.
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification
Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features.
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports.
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.
