A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography

被引：77

作者：

Akselrod-Ballin, Ayelet ^{[1
]}

Karlinsky, Leonid ^{[1
]}

Alpert, Sharon ^{[1
]}

Hasoul, Sharbell ^{[1
]}

Ben-Ari, Rami ^{[1
]}

Barkan, Ella ^{[1
]}

机构：

[1] IBM Res Haifa, Haifa, Israel

来源：

DEEP LEARNING AND DATA LABELING FOR MEDICAL APPLICATIONS | 2016年 / 10008卷

关键词：

D O I：

10.1007/978-3-319-46976-8_21

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses the problem of detection and classification of tumors in breast mammograms. We introduce a novel system that integrates several modules including a breast segmentation module and a fibroglandular tissue segmentation module into a modified cascaded region-based convolutional network. The method is evaluated on a large multi-center clinical dataset and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health-care continues to accelerate generalizing such an approach may have a profound impact on patient care in many applications.

引用

页码：197 / 205

页数：9

共 17 条

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