A shell and kernel descriptor based joint deep learning model for predicting breast lesion malignancy

Zhiguo Zhou, Genggeng Qin, Pingkun Yan, Hongxia Hao, Steve Jiang, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Predicting lesion malignancy accurately and reliably in digital breast tomosynthesis is critically important for breast cancer screening. Tumor shape and interactive effect between the tumor and surrounding normal tissue are two of the most important indicators in radiologists' reading. On the other hand, the density and texture of region within the tumor also play an important role in malignancy classification. Inspired by the above observations, shell and kernel descriptors were proposed in this work for breast lesion malignancy prediction, in which the shell descriptor is used for describing the tumor shape and surrounding normal tissue while the kernel descriptor is used to describe the internal tumor region. A joint deep learning model based on the AlexNet was designed to learn and fuse features from shell and kernel. Additionally, to obtain more reliable predictive results, a multi-objective optimization algorithm and a reliable classifier fusion strategy were used to train the predictive model and optimally combine outputs from both shell and kernel descriptors. In this study, 278 malignant and 685 benign cases were used through 2-fold cross validation. Compared with the single descriptor based models using either shell or kernel, the experimental results demonstrated that the combined shell and kernel descriptors can capture the most important features and the corresponding predictive model achieved the best performance as well.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
PublisherSPIE
ISBN (Electronic)9781510625471
DOIs
Publication statusPublished - Jan 1 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10950
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego
Period2/17/192/20/19

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Keywords

  • Breast lesion malignancy
  • Joint deep learning
  • Kernel descriptor
  • Shell descriptor

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Zhou, Z., Qin, G., Yan, P., Hao, H., Jiang, S., & Wang, J. (2019). A shell and kernel descriptor based joint deep learning model for predicting breast lesion malignancy. In K. Mori, & H. K. Hahn (Eds.), Medical Imaging 2019: Computer-Aided Diagnosis [109502S] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950). SPIE. https://doi.org/10.1117/12.2512277