Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer...
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning mode...
OBJECTIVE: The present study aims to explore the role of smoking factors in the risk of lung cancer and screen the feature risk pathways of smoking-induced lung cancer.
Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-gla...
PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer.
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, ar...
Computer methods and programs in biomedicine
May 22, 2020
BACKGROUND AND OBJECTIVE: In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning and survival rate. Invasive pathological tests for the classification of MLNs into benign and malig...
Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the ...
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