Search
Now showing items 1-10 of 15
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 ...
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. ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...