Skip to main content
placeholder image

Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks

Journal Article


Abstract


  • This work addresses a novel computer-aided diagnosis (CAD) system in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed based on a mixture ensemble of convolutional neural networks (ME-CNN) to discriminate between benign and malignant breast tumors. The ME-CNN is a modular and image-based ensemble, which can stochastically partition the high-dimensional image space through simultaneous and competitive learning of its modules. The proposed system was assessed on our database of 112 DCE-MRI studies including solid breast masses, using a wide range of classification measures. The ME-CNN model composed of three CNN experts and one convolutional gating network achieves an accuracy of 96.39%, a sensitivity of 97.73% and a specificity of 94.87%. The experimental results also show that it has competitive classification performances compared to three existing single-classifier methods and two convolutional ensemble methods. The proposed ME-CNN model could provide an effective tool for radiologists to analyse breast DCE-MRI images.

Authors


  •   Rasti, Reza (external author)
  •   Teshnehlab, Mohammad (external author)
  •   Phung, Son Lam.

Publication Date


  • 2017

Citation


  • R. Rasti, M. Teshnehlab & S. L. Phung, "Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks," Pattern Recognition, vol. 72, pp. 381-390, 2017.

Scopus Eid


  • 2-s2.0-85027533039

Number Of Pages


  • 9

Start Page


  • 381

End Page


  • 390

Volume


  • 72

Place Of Publication


  • Netherlands

Abstract


  • This work addresses a novel computer-aided diagnosis (CAD) system in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed based on a mixture ensemble of convolutional neural networks (ME-CNN) to discriminate between benign and malignant breast tumors. The ME-CNN is a modular and image-based ensemble, which can stochastically partition the high-dimensional image space through simultaneous and competitive learning of its modules. The proposed system was assessed on our database of 112 DCE-MRI studies including solid breast masses, using a wide range of classification measures. The ME-CNN model composed of three CNN experts and one convolutional gating network achieves an accuracy of 96.39%, a sensitivity of 97.73% and a specificity of 94.87%. The experimental results also show that it has competitive classification performances compared to three existing single-classifier methods and two convolutional ensemble methods. The proposed ME-CNN model could provide an effective tool for radiologists to analyse breast DCE-MRI images.

Authors


  •   Rasti, Reza (external author)
  •   Teshnehlab, Mohammad (external author)
  •   Phung, Son Lam.

Publication Date


  • 2017

Citation


  • R. Rasti, M. Teshnehlab & S. L. Phung, "Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks," Pattern Recognition, vol. 72, pp. 381-390, 2017.

Scopus Eid


  • 2-s2.0-85027533039

Number Of Pages


  • 9

Start Page


  • 381

End Page


  • 390

Volume


  • 72

Place Of Publication


  • Netherlands