Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.

Hongmin Cai,Jinhua Wang,Wentao Rong,Li Li,Jiazhou Chen,Jiao Li,Yan Song,Qinjian Huang

doi:10.1155/2019/2717454

Abstract

Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mammography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performance is highly dependent on handcrafted image descriptors. Characterizing the calcification mammography in an automatic and robust way remains a challenge. In this paper, the calcification was characterized by descriptors obtained from deep learning and handcrafted descriptors. We compared the performances of different image feature sets on digital mammograms. The feature sets included the deep features alone, the handcrafted features, their combination, and the filtered deep features. Experimental results have demonstrated that the deep features outperform handcrafted features, but the handcrafted features can provide complementary information for deep features. We achieved a classification precision of 89.32% and sensitivity of 86.89% using the filtered deep features, which is the best performance among all the feature sets.

Highlights

Breast cancer is the most common cancer affecting women’s health
We have made advances toward the end-to-end training of a deep convolutional neural network (CNN) for microcalcification discrimination for breast cancer screening. e images were collected from two distinct medical institutions
The discrepancy map for the neuron associated with the traditional radiomics The sensitive region for the neuron associated with the traditional radiomics

Summary

Introduction

Detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients’ lifespan [1]. It is widely used to diagnose breast disease at an early stage due to its high sensitivity. The presence of breast microcalcifications (MCs) is a primary risk factor for breast cancer. Breast calcifications in the early stages of breast cancer appear like scattered spots in the mammographic image that range from 0.1 to 1.0 mm in size [2]. Because a high correlation has been observed between the appearance of calcification clusters and pathology results, the MCs provide a standard and effective way for the automated detection of breast tumors

Methods

Results

Conclusion