Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.

Journal: Academic radiology
Published Date: Aug 1, 2018

Abstract

RATIONALE AND OBJECTIVES: We evaluate utilizing convolutional neural networks (CNNs) to optimally fuse parenchymal complexity measurements generated by texture analysis into discriminative meta-features relevant for breast cancer risk prediction.

Authors

  • Aimilia Gastounioti
    Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
  • Andrew Oustimov
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104.
  • Meng-Kang Hsieh
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104.
  • Lauren Pantalone
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104.
  • Emily F Conant
    Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).
  • Despina Kontos
    Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States.

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