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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 ...
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) ...
LightX3ECG: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification
(2022-07-25)
Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays ...
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 ...
Enhancing few-shot image classification with cosine transformer
(2023-07-21)
This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot ...
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 ...
FEDGRAD: Mitigating backdoor attacks in federated learning through local ultimate gradients inspection
(2023-04-29)
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. However, the decentralized nature of FL makes it susceptible to adversarial attacks, particularly backdoor insertion ...
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022-03-20)
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and ...
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 ...