ISBI 2018 – How to fool radiologsits with GAN?

Our “visual Turing test” paper was accepted for publication in IEEE ISBI 2018.

We generated realistic lung nodules from CT images for lung cancer screening experiments. We have conducted two experiments in fact: 1) fake/real nodule differentiation by radiologists, 2) malignant/benign separation tests. Specifically, 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 through highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.

Here is the PDF of the paper –>



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