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VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
(2021-06-24)
Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at ...
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
(2021-10-17)
Chest radiograph (CXR) interpretation is critical for the diagnosis of various thoracic diseases in pediatric patients. This task, however, is error-prone and requires a high level of understanding of radiologic expertise. ...
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
(2022-10-07)
Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence ...
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
(2022-08-06)
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 ...
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
(2022-03-20)
Image augmentation techniques have been extensively studied to enhance the performance of deep learning (DL) algorithms in mammography classification tasks. Recent advancements have demonstrated the effectiveness of image ...
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022)
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 ...
VinDr-Mammo: A large-scale benchmark dataset for computer- aided diagnosis in full-feld 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) ...
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
(2023)
We propose for the first time a new strategy to train slice-level classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional ...
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
(2022-12)
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 ...