  Accurate Computer-Assisted Diagnosis, associated with proper data wrangling,
can alleviate the risk of overlooking the diagnosis in a clinical environment.
Towards this, as a Data Augmentation (DA) technique, Generative Adversarial
Networks (GANs) can synthesize additional training data to handle the
small/fragmented medical imaging datasets collected from various scanners;
those images are realistic but completely different from the original ones,
filling the data lack in the real image distribution. However, we cannot easily
use them to locate disease areas, considering expert physicians' expensive
annotation cost. Therefore, this paper proposes Conditional Progressive Growing
of GANs (CPGGANs), incorporating highly-rough bounding box conditions
incrementally into PGGANs to place brain metastases at desired positions/sizes
on 256 X 256 Magnetic Resonance (MR) images, for Convolutional Neural
Network-based tumor detection; this first GAN-based medical DA using automatic
bounding box annotation improves the training robustness. The results show that
CPGGAN-based DA can boost 10% sensitivity in diagnosis with clinically
acceptable additional False Positives. Surprisingly, further tumor realism,
achieved with additional normal brain MR images for CPGGAN training, does not
contribute to detection performance, while even three physicians cannot
accurately distinguish them from the real ones in Visual Turing Test.

Computer Vision and Pattern Recognition;Machine Learning