A multi-stage fusion strategy for multi-scale GLCM-CNN model in differentiating malignant from benign polyps

Jiaxing Tan, Shu Zhang, Weiguo Cao, Yongfeng Gao, Lihong Li, Yumei Huo, Zhengrong Liang

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Computer aided diagnosis (CADx) of polyps has shown great potential to advance the computed tomography colonography (CTC) technique with diagnostic capability. Facing the problem of numerous uncertainties such as polyp size, shape, and orientation in CTC, GLCM-CNN has been proved to be an effective deep learning based tumor classification method, where convolution neural network (CNN) makes decision based on the texture pattern encoded in gray level co-occurrence matrix (GLCM) containing 13 directions. The 13 directional GLCM, by sampling displacement, can be classified into 3 subgroups. Based on our evaluation on the information encoded in the three subgroups, we propose a multi-stage fusion CNN model, which makes the final decision based on two types of features, i.e. (1) a gate module selected group-specific features and (2) fused features learnt from all the features from three groups. On our polyp dataset, which contains 87 polyp masses, our proposed method outperforms both single sub-group based and 13 directional GLCM based CNN model by at least 1.3% in AUC by the average of 20 times 2 fold cross validation experiment results.

源语言英语
主期刊名Medical Imaging 2020
主期刊副标题Computer-Aided Diagnosis
编辑Horst K. Hahn, Maciej A. Mazurowski
出版商SPIE
ISBN(电子版)9781510633957
DOI
出版状态已出版 - 2020
已对外发布
活动Medical Imaging 2020: Computer-Aided Diagnosis - Houston, 美国
期限: 16 2月 202019 2月 2020

出版系列

姓名Progress in Biomedical Optics and Imaging - Proceedings of SPIE
11314
ISSN(印刷版)1605-7422

会议

会议Medical Imaging 2020: Computer-Aided Diagnosis
国家/地区美国
Houston
时期16/02/2019/02/20

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