ConTenNet: Quantum Tensor-augmented Convolutional Representations for Breast Cancer Histopathological Image Classification

Abstract

In recent years, deep convolutional neural networks (CNNs) have been spectacularly successful in the classification and diagnosis of breast cancer and its histopathological images. However, for CNNs, the whole learning process requires high computational complexity, a large number of parameters, and loss of certain global feature information. Meanwhile, the flexibility of tensor networks (TNs) algorithms to machine learning leads to creativity in devising new approaches. In this paper, we propose a novel framework named ConTenNet based on the pre-trained CNNs and quantum TNs (QTNs) to address the weaknesses in CNNs. We propose ConTenNet on the BreakHis dataset, and the experiments show that our model competes with the state-of-the-art methods on both original and normalized images with lower computational complexity, a less number of parameters, and global feature information. Moreover, we adopt the color normalization method to avoid the interference of color in model learning, using the gradient-weighted class activation mapping (Grad-CAM) to prove the necessity of color normalization and the reliability of model learning.