A transparent cancer classifier. (March 2020)

• Record Type: Journal Article
• Authors: Hartono, Pitoyo
• Other Names: Bian Jiang guest-editor.; Modave Francois guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 1(2020)
• Page Start: 190
• Page End: 204
• Publication Date: 2020-03
• Subjects: cancer diagnosis -- microarray gene expression data -- neural network -- visualization; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458218817800
• Languages: English
• ISSNs: 1460-4582

Abstract

Recently, many neural network models have been successfully applied for histopathological analysis, including for cancer classifications. While some of them reach human–expert level accuracy in classifying cancers, most of them have to be treated as black box, in which they do not offer explanation on how they arrived at their decisions. This lack of transparency may hinder the further applications of neural networks in realistic clinical settings where not only decision but also explainability is important. This study proposes a transparent neural network that complements its classification decisions with visual information about the given problem. The auxiliary visual information allows the user to some extent understand how the neural network arrives at its decision. The transparency potentially increases the usability of neural networks in realistic histopathological analysis. In the experiment, the accuracy of the proposed neural network is compared against some existing classifiers, and the visual information is compared against some dimensional reduction methods.