AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage.
Computer methods and programs in biomedicine
Jun 20, 2020
BACKGROUND AND OBJECTIVE: To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection ...
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
Computer methods and programs in biomedicine
Jun 20, 2020
BACKGROUND AND OBJECTIVE: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cas...
Deep Learning
Image Processing, Computer-Assisted
Radiographic Image Interpretation, Computer-Assisted
Lung Diseases
Algorithms
Reproducibility of Results
SARS-CoV-2
Pneumonia, Viral
Coronavirus Infections
Radiography, Thoracic
X-Rays
Betacoronavirus
Humans
Neural Networks, Computer
Pandemics
COVID-19
Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jun 20, 2020
PURPOSE: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-ba...
Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast.
Clinical breast cancer
Jun 7, 2020
INTRODUCTION: We previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further va...
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.
Computer methods and programs in biomedicine
Jun 5, 2020
BACKGROUND AND OBJECTIVE: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As...
Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium.
European journal of radiology
Jun 3, 2020
PURPOSE: Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which ...
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.
Proceedings of the National Academy of Sciences of the United States of America
May 26, 2020
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research...
Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.
European journal of radiology
May 23, 2020
PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer.
A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
European journal of radiology
May 20, 2020
PURPOSE: To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC).
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.
The Lancet. Digital health
May 14, 2020
BACKGROUND: CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aim...
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