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Found 13 papers, 2 papers with code
Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings
Several evaluation metrics have been developed recently to automatically assess the quality of generative AI reports for chest radiographs based only on textual information using lexical, semantic, or clinical named entity recognition methods.
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention.
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 hampered by the lack of locally labeled datasets for different findings.
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision.
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today.
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
Chest X-ray Report Generation through Fine-Grained Label Learning
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note.
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications.
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
We experiment with two different deep neural network architectures and show that the proposed method results in a large improvement in the performance of the classifiers, specially on negated findings.
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals.
Age prediction using a large chest X-ray dataset
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades.
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
We assess the performance of deep learning algorithms and compare them with classical NLP approaches.
