Bag of Deep Features for Classification of Gigapixel Histological Images

Abstract

Convolutional Neural Networks (CNNs) have proven to be one of the most powerful tools for solving complex problems in the field of pattern recognition and image analysis, even if serious challenges remain. Indeed, one of the main drawbacks of CNNs is their inability to cope with very high-resolution images. In areas other than digital pathology, image resizing is often the simplest and most effective solution. However, histopathological images not only show a very high resolution, but also contain a lot of information at the detail level, making this strategy completely ineffective. Other approaches partition the image into small patches and analyze them independently, losing the context information that is fundamental in digital pathology. In this paper, we present a method based on a compressed representation of the Whole Slide Image (WSI), by building a 3D tensor, that preserves the topological and morphological information relating to the proximity relationships between the patches of the WSI. Tensors are used to train a CNN to solve a binary classification task. This technique has been evaluated for the analysis of gigapixel Hematoxylin and Eosin (H&E) histological images with the aim of supporting the diagnosis of breast cancer. Several experiments have been performed on the Camelyon16 dataset by generating different types of 3D tensors. The results of the proposed approach on the breast cancer classification task have been compared with some state-of-the-art approaches.