Breast Cancer Histopathological Image Classification: Is Magnification Important?

Vibha Gupta, Arnav Bhavsar; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 17-24

Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which utilizes joint colour-texture features and a classifier ensemble for classifying breast histopathology images. While we demonstrate the effectiveness of the proposed framework, an important objective of this work is to study the image classification across different optical magnification levels. We provide interesting experimental results and related discussions, demonstrating a visible classification invariance with cross-magnification training-testing. Along with magnification-specific model, we also evaluate the magnification independent model, and compare the two to gain some insights.