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

Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection