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04/01/2023
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
We conducted a prospective study to measure the clinical impact of an ex...
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03/29/2023
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
The work discusses the use of machine learning algorithms for anomaly de...
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09/11/2022
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
We propose a data collecting and annotation pipeline that extracts infor...
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08/06/2022
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
Recent artificial intelligence (AI) algorithms have achieved radiologist...
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08/05/2022
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
The rapid development in representation learning techniques and the avai...
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03/20/2022
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling
A fully automated system for interpreting abdominal computed tomography ...
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03/20/2022
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Mammography, or breast X-ray, is the most widely used imaging modality t...
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03/20/2022
VinDr-PCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
Computer-aided diagnosis systems in adult chest radiography (CXR) have r...
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03/20/2022
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
Building an accurate computer-aided diagnosis system based on data-drive...
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03/20/2022
Transparency strategy-based data augmentation for BI-RADS classification of mammograms
Image augmentation techniques have been widely investigated to improve t...
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12/08/2021
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
Advanced deep learning (DL) algorithms may predict the patient's risk of...
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08/14/2021
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans
X-ray imaging in DICOM format is the most commonly used imaging modality...
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08/14/2021
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Chest radiograph (CXR) interpretation in pediatric patients is error-pro...
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07/03/2021
VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic...
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06/24/2021
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
Radiographs are used as the most important imaging tool for identifying ...
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05/25/2020
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
The chest X-rays (CXRs) is one of the views most commonly ordered by rad...
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05/22/2020
A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
We propose a novel method that combines a convolutional neural network (...
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11/15/2019
Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels
Chest radiography is one of the most common types of diagnostic radiolog...
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09/06/2017
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
We present a new method for image reconstruction which replaces the proj...
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05/16/2017