Title

How To Fool Radiologists With Generative Adversarial Networks? A Visual Turing Test For Lung Cancer Diagnosis

Keywords

Computed Tomography (CT); Computer Aided Diagnosis (CAD) systems; Deep learning; Generated samples; Generative Adversarial Networks (GANs); Lung nodules; Visual Turing Test

Abstract

Discriminating lung nodules as malignant or benign is still an underlying challenge. To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules. However, learning highly discriminative imaging features is an open problem. In this paper, our aim is to learn the most discriminative features pertaining to lung nodules by using an adversarial learning methodology. Specifically, we propose to use un-supervised learning with Deep Convolutional-Generative Adversarial Networks (DC-GANs) to generate lung nodule samples realistically. We hypothesize that imaging features of lung nodules will be discriminative if it is hard to differentiate them (fake) from real (true) nodules. To test this hypothesis, we present Visual Turing tests to two radiologists in order to evaluate the quality of the generated (fake) nodules. Extensive comparisons are performed in discerning real, generated, benign, and malignant nodules. This experimental set up allows us to validate the overall quality of the generated nodules, which can then be used to (1) improve diagnostic decisions by mining highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.

Publication Date

5-23-2018

Publication Title

Proceedings - International Symposium on Biomedical Imaging

Volume

2018-April

Number of Pages

240-244

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISBI.2018.8363564

Socpus ID

85048118763 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85048118763

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