Siamese Network for Dual-View Mammography Mass Matching

被引：11
|
作者：
Perek, Shaked [1 ]
Hazan, Alon [1 ]
Barkan, Ella [1 ]
Akselrod-Ballin, Ayelet [1 ]
机构：
[1] IBM Res, Haifa, Israel
来源：
IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES | 2018年 / 11040卷
关键词：
Biomedical imaging; Deep learning; Mammography; CONVOLUTIONAL NEURAL-NETWORKS; IMAGE-ANALYSIS;
D O I：
10.1007/978-3-030-00946-5_6
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In a standard mammography screening procedure, two Xray images are acquired per breast from two views. In this paper, we introduce a patch based, deep learning network for lesion matching in dual-view mammography using a Siamese network. Our method is evaluated on several datasets, among them the large freely available digital database for screening mammography (DDSM). We perform a comprehensive set of experiment, focusing on the mass correspondence problem. We analyze the effect of transfer learning between different types of dataset, compare the network based matching to classic template matching and evaluate the contribution of the matching network to the detection task. Experimental results show the promise in improving detection accuracy by our approach.
引用
收藏
页码：55 / 63
页数：9
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