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A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs
While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied.
Levels of Autonomous Radiology
Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today.
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations
We investigate the effect of image augmentations on reducing the requirement of labelled data in the semantic segmentation of chest X-rays for pneumonia detection.
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 interpretation of radiology images has gained widespread attention in recent years.
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans
We also present a comparison based on the segmentation results, the contextual information retained, and the inference time between this 3D technique and a traditional 2D deep learning technique.
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.
Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003).
Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening
Edge devices are revolutionizing diagnostics.
Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks
Our method works in two connected stages: in the first stage, an object detection model segments individual knees from the rest of the image; in the second stage, a regression model automatically grades each knee separately on the KL scale.
