Small lesion classification in dynamic contrast enhancement MRI for breast cancer early detection

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Title: Small lesion classification in dynamic contrast enhancement MRI for breast cancer early detection
Authors: Zheng, H
Gu, Y
Qin, Y
Huang, X
Yang, J
Yang, GZ
Item Type: Conference Paper
Abstract: Classification of small lesions is of great importance for early detection of breast cancer. The small size of lesion makes handcrafted features ineffective for practical applications. Furthermore, the relatively small data sets also impose challenges on deep learning based classification methods. Dynamic Contrast Enhancement MRI (DCE-MRI) is widely-used for women at high risk of breast cancer, and the dynamic features become more important in the case of small lesion. To extract more dynamic information, we propose a method for processing sequence data to encode the DCE-MRI, and design a new structure, dense convolutional LSTM, by adding a dense block in convolutional LSTM unit. Faced with the huge number of parameters in deep neural network, we add some semantic priors as constrains to improve generalization performance. Four latent attributes are extracted from diagnostic reports and pathological results, and are predicted together with the classification of benign or malignant. Predicting the latent attributes as auxiliary tasks can help the training of deep neural network, which makes it possible to train complex network with small size dataset and achieve a satisfactory result. Our methods improve the accuracy from 0.625, acquired by ResNet, to 0.847.
Issue Date: 26-Sep-2018
Date of Acceptance: 16-Sep-2018
URI: http://hdl.handle.net/10044/1/65351
DOI: https://dx.doi.org/10.1007/978-3-030-00934-2_97
ISBN: 9783030009335
ISSN: 0302-9743
Publisher: Springer Nature Switzerland AG
Start Page: 876
End Page: 884
Journal / Book Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 11071 LNCS
Copyright Statement: © 2018 Springer Nature Switzerland AG. The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-030-00934-2_97
Conference Name: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Keywords: 08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Finish Date: 2018-09-20
Conference Place: Granada, Spain
Embargo Date: 2019-09-26
Online Publication Date: 2018-09-26
Appears in Collections:Faculty of Engineering
Computing



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