Chexagent: Towards a foundation model for chest x-ray interpretation
Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice.
Recent advances in the development of vision-language foundation models (FMs) give rise …
Recent advances in the development of vision-language foundation models (FMs) give rise …
Cxr-llava: Multimodal large language model for interpreting chest x-ray images
S Lee, J Youn, M Kim, SH Yoon - arXiv preprint arXiv:2310.18341, 2023 - arxiv.org
Purpose: Recent advancements in large language models (LLMs) have expanded their
capabilities in a multimodal fashion, potentially replicating the image interpretation of human …
capabilities in a multimodal fashion, potentially replicating the image interpretation of human …
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
In this work, we present an approach, which we call Embeddings for Language/Image-
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …
aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or …
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
Radiologists highly desire fully automated versatile AI for medical imaging interpretation.
However the lack of extensively annotated large-scale multi-disease datasets has hindered …
However the lack of extensively annotated large-scale multi-disease datasets has hindered …
Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT
This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence
(AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR) …
(AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR) …
Supervised and unsupervised language modelling in Chest X-Ray radiological reports
Chest radiography (CXR) is the most commonly used imaging modality and deep neural
network (DNN) algorithms have shown promise in effective triage of normal and abnormal …
network (DNN) algorithms have shown promise in effective triage of normal and abnormal …
Improving joint learning of chest x-ray and radiology report by word region alignment
Z Ji, MA Shaikh, D Moukheiber, SN Srihari… - Machine Learning in …, 2021 - Springer
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their
associated free-text reports accumulated in clinical routine without manual supervision. This …
associated free-text reports accumulated in clinical routine without manual supervision. This …
Chest imagenome dataset for clinical reasoning
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR)
images in recent years, a quantitative evaluation of the explainability of these models is …
images in recent years, a quantitative evaluation of the explainability of these models is …
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
Evaluating generated radiology reports is crucial for the development of radiology AI, but
existing metrics fail to reflect the task's clinical requirements. This study proposes a novel …
existing metrics fail to reflect the task's clinical requirements. This study proposes a novel …
Maira-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …