  A computer-aided detection (CAD) system based on machine learning is expected
to assist radiologists in making a diagnosis. It is desirable to build CAD
systems for the various types of diseases accumulating daily in a hospital. An
obstacle in developing a CAD system for a disease is that the number of medical
images is typically too small to improve the performance of the machine
learning model. In this paper, we aim to explore ways to address this problem
through a sim2real transfer approach in medical image fields. To build a
platform to evaluate the performance of sim2real transfer methods in the field
of medical imaging, we construct a benchmark dataset that consists of $101$
chest X-images with difficult-to-identify pneumonia lesions judged by an
experienced radiologist and a simulator based on fractal Perlin noise and the
X-ray principle for generating pseudo pneumonia lesions. We then develop a
novel domain randomization method, called Goldilocks-curriculum domain
randomization (GDR) and evaluate our method in this platform.
