Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

Andrew Oustimov, Aimilia Gastounioti, Meng Kang Hsieh, Lauren Pantalone, Emily F. Conant, Despina Kontos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


We assess the feasibility of a parenchymal texture feature fusion approach, utilizing a convolutional neural network (ConvNet) architecture, to benefit breast cancer risk assessment. Hypothesizing that by capturing sparse, subtle interactions between localized motifs present in two-dimensional texture feature maps derived from mammographic images, a multitude of texture feature descriptors can be optimally reduced to five meta-features capable of serving as a basis on which a linear classifier, such as logistic regression, can efficiently assess breast cancer risk. We combine this methodology with our previously validated lattice-based strategy for parenchymal texture analysis and we evaluate the feasibility of this approach in a case-control study with 424 digital mammograms. In a randomized split-sample setting, we optimize our framework in training/validation sets (N=300) and evaluate its descriminatory performance in an independent test set (N=124). The discriminatory capacity is assessed in terms of the the area under the curve (AUC) of the receiver operator characteristic (ROC). The resulting meta-features exhibited strong classification capability in the test dataset (AUC = 0.90), outperforming conventional, non-fused, texture analysis which previously resulted in an AUC=0.85 on the same case-control dataset. Our results suggest that informative interactions between localized motifs exist and can be extracted and summarized via a fairly simple ConvNet architecture.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
EditorsNicholas A. Petrick, Samuel G. Armato
ISBN (Electronic)9781510607132
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2017: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Breast cancer risk
  • Convolutional neural network
  • Digital mammography
  • Parenchymal texture


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