AI Medical Compendium Journal:
Diagnostic and interventional imaging

Showing 51 to 60 of 82 articles

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomograp...

Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4.

Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.

Three artificial intelligence data challenges based on CT and ultrasound.

Diagnostic and interventional imaging
PURPOSE: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant proble...

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitio...

Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.

Artificial intelligence solution to classify pulmonary nodules on CT.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm or not, using machine learning and deep learning techniques.

A primer for understanding radiology articles about machine learning and deep learning.

Diagnostic and interventional imaging
The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major i...