OBJECTIVE: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest.
PURPOSE: To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e....
Clinical cancer research : an official journal of the American Association for Cancer Research
Feb 24, 2021
PURPOSE: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making.
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to inve...
OBJECTIVE: To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.
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.
Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and ...
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 1, 2020
BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a chal...