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 unsupervised 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.
Chuquicusma MJ et al. REF conducted visual Turing tests to evaluate the degree of realism in nodule images generated by a DCGAN and showed that the generated samples can be used to boost the diagnostic power by mining high-level discriminative image features and that the resulting features can be used to train both radiologists and deep networks.
How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Computer Science Biology