Supervised intra-embedding of fisher vectors for histopathology image classification

Abstract

In this paper, we present a histopathology image classification method with supervised intra-embedding of Fisher vectors. Recently in general computer vision, Fisher encoding combined with convolutional neural network (ConvNet) has become popular as a highly discriminative feature descriptor. However, Fisher vectors have two intrinsic problems that could limit their performance: high dimensionality and bursty visual elements. To address these problems, we design a novel supervised intra-embedding algorithm with a multilayer neural network model to transform the ConvNet-based Fisher vectors into a more discriminative feature representation. We apply this feature encoding method on two public datasets, including the BreaKHis image dataset of benign and malignant breast tumors, and IICBU 2008 lymphoma dataset of three malignant lymphoma subtypes. The results demonstrate that our supervised intra-embedding method helps to enhance the ConvNet-based Fisher vectors effectively, and our classification results largely outperform the state-of-the-art approaches on these datasets.