@inproceedings{e8ecb97a153a47bf91afab849db7802c,
title = "Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images",
abstract = "Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model which assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since hierarchical model restricts classification error to each sub-class, it leads to a more informative model compared to a flat model. In this paper we propose to apply hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of the hierarchical structure of its layers. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that hierarchical model can achieve better results compared to the flat model for multi-category diagnosis of GI disorders using histopathological images.",
keywords = "Coarse categories, Fine classes, Gastrointestinal disorders, Hierarchical deep convolutional neural network, Histopathological images, Multi-category diagnosis",
author = "Rasoul Sali and Sodiq Adewole and Lubaina Ehsan and Denson, {Lee A.} and Paul Kelly and Amadi, {Beatrice C.} and Lori Holtz and Ali, {Syed Asad} and Moore, {Sean R.} and Sana Syed and Brown, {Donald E.}",
note = "Funding Information: Research reported in this manuscript was supported by National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award number K23DK117061-01A1 (SS), Bill and Melinda Gates Foundation under award numbers OPP1066203, OPP1066118, OPP1144149 and OPP1066153 and University of Virginia Translational Health Research Institute of Virginia (THRIV) Scholar Career Development Award (SS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Healthcare Informatics, ICHI 2020 ; Conference date: 30-11-2020 Through 03-12-2020",
year = "2020",
month = nov,
doi = "10.1109/ICHI48887.2020.9374332",
language = "English",
series = "2020 IEEE International Conference on Healthcare Informatics, ICHI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Healthcare Informatics, ICHI 2020",
}