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Found 8 papers, 1 papers with code
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Furthermore, we demonstrate that one of our checkpoints exhibits a high correlation with human judgment, as assessed using the publicly available annotations of six board-certified radiologists, using a set of 200 reports.
Development of pericardial fat count images using a combination of three different deep-learning models
A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method.
Boosting Radiology Report Generation by Infusing Comparison Prior
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks
The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner facilitates the simultaneous acquisition of metabolic information via PET and morphological information with high soft-tissue contrast using MRI.
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images.
Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model
To assess the detection model's results, a board-certified radiologist also evaluated the test set head CT images with and without the aid of the detection model.
Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning
Results: Our results demonstrated that using baseline U-net yielded poorer lung segmentation results in our database than those in the JSRT and Montgomery databases, implying that robust segmentation of lungs may be difficult because of severe abnormalities.
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
TPE or random search was used for parameter optimization of SVM and XGBoost.
