Paper Info
Title
Breast Cancer Detection Based On Merging Four Modes Mri Using Convolutional Neural Networks
Abstract
The objective of the study is to develop a framework for automatic breast cancer detection with merging four imaging modes. Attempts were made for tumor classification and segmentation; using a multi-parametric Magnetic Resonance Imaging (MRI) method on breast tumors. MRI data of the breast were obtained from 67 subjects with a 1.5T-MRI scanner. Four imaging modes: were T1 weighted, T2 weighted, Diffusion Weighted and eTHRIVE sequences, and dynamic-contrast- enhanced(DCE)-MRI parameters are acquired. The proposed four-mode linkage backbone in tumor classification, which overcomes the limitations of single-modality image detection and simulates actual diagnosis processes by clinicians, achieves the accuracy of 0.942. The proposed automatic segmentation approach is performed by a refined U-Net architecture, and the result improved segmentation performance significantly. The combination of four-mode linkage classification backbone and improved segmentation network for breast cancer detection forms a computer-aided detection (CAD) system that corresponds to the actual clinical diagnosis work.
Year
DOI
Venue
2019
10.1109/icassp.2019.8683149
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
four-mode linkage, classification, convolutional neural network, segmentation, MRI
CAD,Breast cancer,Pattern recognition,Computer science,Convolutional neural network,Segmentation,Feature extraction,Scanner,Artificial intelligence,Merge (version control),Magnetic resonance imaging
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Wenhuan Lu153.83
Zhe Wang26424.17
Yuqing He36319.58
Hong Yu41982179.13
Naixue Xiong52413194.61
Jianguo Wei612.38