@inproceedings{9b670625f8194699a025bb21af260b6b,
title = "Lung nodule classification by jointly using visual descriptors and deep features",
abstract = "Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.",
keywords = "Computed tomography, Deep convolutional neural network, Lung nodule classification, Shape descriptor, Texture descriptor",
author = "Yutong Xie and Jianpeng Zhang and Sidong Liu and Weidong Cai and Yong Xia",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-61188-4_11",
language = "English",
isbn = "9783319611877",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-VDI-Verlag GmbH & Co. KG",
pages = "116--125",
editor = "Henning Muller and Kelm, {B. Michael} and Tal Arbel and Weidong Cai and Cardoso, {M. Jorge} and Georg Langs and Bjoern Menze and Dimitris Metaxas and Albert Montillo and {Wells III}, {William M.} and Shaoting Zhang and Chung, {Albert C. S.} and Mark Jenkinson and Annemie Ribbens",
booktitle = "Medical computer vision and Bayesian and graphical models for biomedical imaging",
address = "Germany",
note = "International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",
}