To investigate the performance of a joint convolutional neural networks-recurrent neural
networks (CNN-RNN) using an attention mechanism in identifying and classifying …

Objectives To evaluate the performance of a novel three-dimensional (3D) joint
convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial …

BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology
for image recognition. This study evaluates a convolutional neural network optimized for the …

Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels
only) and strong supervision (ie, with image-level labels) in training deep learning models …

Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …

Purpose To compare the diagnostic accuracy of convolutional neural networks (CNN) with
radiologists as the reference standard in the diagnosis of intracranial hemorrhages (ICH) …

Purpose We evaluate the performance of a deep learning-based pipeline using a Dense U-
net architecture for detection of intracranial hemorrhage (ICH) in unenhanced head …

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is
of utmost importance to avoid untoward incidents that may even lead to death. Hence, this …

Purpose To analyze the implementation of deep learning software for the detection and
worklist prioritization of acute intracranial hemorrhage on non-contrast head CT (NCCT) in …

Background Highly accurate detection of intracranial hemorrhages (ICH) on head computed
tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to …