作者：
Sun, Haotian;Wu, Shandong;Chen, Xinjian;Li, Ming;Kong, Lingji;...
作者机构：
School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
Gusu School, Nanjing Medical University, Suzhou, China
Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
Department of Radiology, Department of Biomedical Informatics, Department of Bioengineering, Department of Intelligent Systems, and Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA
School of Electronics and Information Engineering and the State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
Department of Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
Gusu School, Nanjing Medical University, Suzhou, China
语种：
英文
期刊：
IEEE Transactions on Cybernetics
ISSN：
2168-2267
年：
2024
卷：
PP
页码：
2345-2357
基金类别：
10.13039/501100010881-Suzhou Science and Technology Bureau (Grant Number: SJC2021023);10.13039/501100007289-Research Project of Gusu School of Nanjing Medical University (Grant Number: GSKY20210227);Applied Basic Research Project in Suzhou (Grant Number: SKJY2021126);Beijing CSCO Clinical Oncology Research Foundation (Grant Number: Y-Young 2021-0087)
摘要：
Benign and malignant classification of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT) is an essential task in computer-aided diagnosis. However, due to the anisotropic resolution of DBT, three-dimensional (3-D) convolutional neural network (CNN)-based methods cannot extract hierarchical features efficiently. Moreover, the sparse distribution of MC points in the cluster makes it difficult for the CNN to extract discriminative structural information for classification. To comprehensively address these challenges, we propose a novel structure-aware hierarchical network (SAH-Net) for benign and malignant classification of clustered MC in a DBT volum...
