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Found 9 papers, 5 papers with code
A Benchmark of Domain-Adapted Large Language Models for Generating Brief Hospital Course Summaries
To enable the adaptation of LLMs for BHC synthesis, we introduce a novel benchmark consisting of a pre-processed dataset extracted from MIMIC-IV notes, encapsulating clinical note, and brief hospital course (BHC) pairs.
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
As an example, we generated 100, 000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes.
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.
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
We propose using a coordinate network decoder for the task of super-resolution in MRI.
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.
