论文标题
使用SIM2REAL肺炎病变检测的Goldilocks-Curriculum结构域随机化和分形Perlin噪声
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection
论文作者
论文摘要
基于机器学习的计算机辅助检测系统（CAD）系统有望帮助放射科医生进行诊断。希望为每天在医院积累的各种疾病建立CAD系统。开发用于疾病的CAD系统的障碍是，医学图像的数量通常太小，无法提高机器学习模型的性能。在本文中，我们旨在通过医学图像字段中的SIM2REAL转移方法探讨解决此问题的方法。为了构建一个平台来评估医学成像领域中Sim2real转移方法的性能，我们构建了一个基准数据集，该数据集由$ 101 $ caste x-Images组成，胸部X形图像很难识别经验丰富的放射线医生和基于分形Perlin噪声的模拟器来判断的肺炎病变，以生成Pseudo pseumiaia les les les les les。然后，我们开发了一种新型的域随机化方法，称为Goldilocks-Curriculum域随机化（GDR），并在此平台中评估我们的方法。
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.
