Browsing by Subject "deep learning"
Now showing items 1-17 of 17
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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 ... -
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 ... -
Benchmarking saliency methods for chest X-ray interpretation
(2022-10)Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation ... -
Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ... -
Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ... -
Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography
(2023-10-13)Background: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for ... -
Few-Shot Learning based on Residual Neural Networks for X-ray Image Classification
(2022)Currently, deep learning is widely used in the field of medicine, which includes radiology. This paper addresses the classification of X-ray images, particularly focusing on the challenge of insufficient images for specific ... -
FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics
(2021-05-13)Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in ... -
FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics
(2021-05-13)Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in ... -
GOAL: Gist-set online active learning for efficient chest X-ray image annotation
(2021)Deep learning in medical image analysis often requires extensive high-quality labeled data to achieve human-level accuracy. We propose Gist-set Online Active Learning (GOAL), a novel solution to the challenge of limited ... -
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. ... -
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 ... -
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 ... -
Optimal auction for effective energy management for UAV-assisted metaverse synchronization system
(2023)In this paper, we investigate an effective energy management in a UAV-assisted Metaverse synchronization system. The UAVs perform the data collection for a virtual service provider (VSP) for the synchronization between the ... -
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 ... -
A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
(2022-01-17)As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in ... -
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 ...
