Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. (December 2017) - Rasti, Reza; Teshnehlab, Mohammad; Phung, Son Lam
Highlights: A new method for breast cancer diagnosis in DCE-MRI is presented. We propose a mixture ensemble of convolutional neural networks for image classification. A convolutional gating network coordinates simultaneous, competitive learning of CNN experts. ME-CNN ensemble model is efficient for biomedical problems with a limited number of samples. The proposed model performs comparatively well on a DCE-MRI dataset of 112 patients. 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.