An artificial intelligence system for predicting mortality in COVID-19 patients using chest X-rays: a retrospective study
Background Early prediction of disease severity in COVID-19 patients is essential.
Automated Detection of COVID-19
from CT Scans Using Convolutional
COVID-19 is an infectious disease that causes respiratory
problems similar to those caused by SARS-CoV (2003).
Survey of Personalization Techniques for Federated Learning
Federated learning enables machine learning models
to learn from private decentralized data without
Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs
The severity of knee osteoarthritis is graded using the 5-point Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the subsequent grades 1-4 represent increasing severity of the affliction.
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans
Image segmentation plays a pivotal role in several medical-imaging applications by assisting the segmentation of the regions of interest.
Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19
TB is a pandemic which has challenged mankind for ages
and Covid 19 is a recent onset fast-spreading pandemic.
Real-world analysis of artificial
intelligence in musculoskeletal trauma
Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room.
Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions
Using Imaging Diagnostics
Edge devices are revolutionizing diagnostics. Edge devices
can reside within or adjacent to imaging tools such as digital
Xray, CT, MRI, or ultrasound equipment.
Quantum Computing Methods for
The last two decades have seen explosive growth in the
theory and practice of both quantum computing and
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed.
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly
We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs.
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years.
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations
Deep learning semantic segmentation algorithms can localize abnormalities or opacities from chest radiographs.