Automated classification of breast cancer histologic grade using multiphoton microscopy and generative adversarial networks

Histological grade is one of the most powerful prognostic factors for breast cancer and impacts treatment decisions. However, a label-free and automated classification system for histological grading of breast tumors has not yet been developed. In this study, we employed label-free multiphoton microscopy (MPM) to acquire subcellular-resolution images of unstained breast cancer tissues. Subsequently, a deep-learning algorithm based on the generative adversarial network (GAN) was introduced to learn a representation using only MPM images without the histological grade information. Furthermore, to obtain abundant image information and determine the detailed differences between MPM images of different grades, a multiple-feature discriminator network based on the GAN was leveraged to learn the multi-scale spatial features of MPM images through unlabeled data. The experimental results showed that the classification accuracies for tumors of grades 1, 2, and 3 were 92.4%, 88.6%, and 89.0%, respectively. Our results suggest that the fusion of multiphoton microscopy and the GAN-based deep learning algorithm can be used as a fast and powerful clinical tool for the computer-aided intelligent pathological diagnosis of breast cancer.

Automated summary

In this study, label-free multiphoton microscopy (MPM) was used to acquire subcellular-resolution images of unstained breast cancer tissues. A deep-learning algorithm based on the generative adversarial network (GAN) was introduced to learn a representation using only MPM images without the histological grade information. The fusion of MPM and the GAN-based deep learning algorithm showed high classification accuracies for different tumor grades, suggesting its potential as a clinical tool for computer-aided intelligent pathological diagnosis of breast cancer.