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Deep convolutional neural networks(CNNs) have been successful for a widerange of computer vision tasks, including image classification. A specific areaof the application lies in digital pathology for pattern recognition in thetissue-based diagnosis of gastrointestinal(GI) diseases. This domain canutilize CNNs to translate histopathological images into precise diagnostics.This is challenging since these complex biopsies are heterogeneous and requiremultiple levels of assessment. This is mainly due to structural similarities indifferent parts of the GI tract and shared features among different gutdiseases. Addressing this problem with a flat model that assumes all classes(parts of the gut and their diseases) are equally difficult to distinguishleads to an inadequate assessment of each class. Since the hierarchical modelrestricts classification error to each sub-class, it leads to a moreinformative model than a flat model. In this paper, we propose to apply thehierarchical classification of biopsy images from different parts of the GItract and the receptive diseases within each. We embedded a class hierarchyinto the plain VGGNet to take advantage of its layers' hierarchical structure.The proposed model was evaluated using an independent set of image patches from373 whole slide images. The results indicate that the hierarchical model canachieve better results than the flat model for multi-category diagnosis of GIdisorders using histopathological images.

Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images