The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodules can greatly increase patient survival. In this paper, we propose a novel classification method for...
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main ...
Journal of the American College of Radiology : JACR
May 24, 2019
PURPOSE: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations.
International journal of computer assisted radiology and surgery
Apr 26, 2019
PURPOSE: Pulmonary nodule detection has great significance for early treating lung cancer and increasing patient survival. This work presents a novel automated computer-aided detection scheme for pulmonary nodules based on deep convolutional neural n...
International journal of computer assisted radiology and surgery
Apr 24, 2019
PURPOSE: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the sh...
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by usi...
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulm...
A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extractio...
Existing deep-learning-based pulmonary nodule classification models usually use images and benign-malignant labels as inputs for training. Image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convo...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.