Context-Sensitive Deep Learning for Detection of Clustered Micro Calcifications in Mammograms

Juan Wang; Yongyi Yang

ICASSP 2018accepted0 citations

Context-Sensitive Deep Learning for Detection of Clustered Micro Calcifications in Mammograms

Abstract

A challenging issue in computerized detection of clustered microcalcifications (MCs) is the frequent occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We develop a context-sensitive deep neural network (DNN) for MC detection, aimed to take into account both the local image features of an MC and its surrounding tissue background. The proposed approach was evaluated on the accuracy both in detecting individual MCs and in detecting MC clusters on a set of 292 mammograms using free-response receiver operating characteristic (FROC) analysis. The results demonstrate that the proposed approach could achieve a significantly higher accuracy in detected individual MCs; incorporating image context information in MC detection can be beneficial for reducing FPs.

BibTeX

@inproceedings{icassp2018_contextsensitive,
  title = {Context-Sensitive Deep Learning for Detection of Clustered Micro Calcifications in Mammograms},
  author = {Juan Wang and Yongyi Yang},
  booktitle = {ICASSP 2018},
  year = {2018}
}