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Now showing items 1-10 of 11
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
(2022)
Advanced deep learning (DL) algorithms may predict the patient’s risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that ...
Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling
(2022-01-31)
Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires accurate classification of the phases. Current approaches typically utilize ...
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2022-03-20)
Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective ...
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
(2023)
Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning ...
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
(2023)
Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning ...
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
(2023)
Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) ...
An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph
(2022-10-04)
Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical ...
Slice-level detection of intracranial hemorrhage on CT using deep descriptors of adjacent slices
(2023-04-17)
The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have led to a rapid increase in the use of supervised ...
A novel transparency strategy-based data augmentation approach for BI-RADS classification of mammograms
(2023-04-17)
Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have proved the efficiency of image augmentation ...
Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling
(2022-03-20)
Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. Current approaches to classify the ...