English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Discriminating lung nodules as malignant or benign is still an underlyingchallenge. To address this challenge, radiologists need computer aideddiagnosis (CAD) systems which can assist in learning discriminative imagingfeatures corresponding to malignant and benign nodules. However, learninghighly discriminative imaging features is an open problem. In this paper, ouraim is to learn the most discriminative features pertaining to lung nodules byusing an adversarial learning methodology. Specifically, we propose to useunsupervised learning with Deep Convolutional-Generative Adversarial Networks(DC-GANs) to generate lung nodule samples realistically. We hypothesize thatimaging features of lung nodules will be discriminative if it is hard todifferentiate them (fake) from real (true) nodules. To test this hypothesis, wepresent Visual Turing tests to two radiologists in order to evaluate thequality of the generated (fake) nodules. Extensive comparisons are performed indiscerning real, generated, benign, and malignant nodules. This experimentalset up allows us to validate the overall quality of the generated nodules,which can then be used to (1) improve diagnostic decisions by mining highlydiscriminative imaging features, (2) train radiologists for educationalpurposes, and (3) generate realistic samples to train deep networks with bigdata.

How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis