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

arXiv: https://arxiv.org/abs/2204.13849 · HTML: https://mlsanity.com/paper/2204.13849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Understanding the Role of Training Regimes in Continual Learning](https://mlsanity.com/paper/2006.06958/) (2020)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [When Do Curricula Work?](https://mlsanity.com/paper/2012.03107/) (2020)
- [Data-efficient Domain Randomization with Bayesian Optimization](https://mlsanity.com/paper/2003.02471/) (2020)
- [Learning To Simulate](https://mlsanity.com/paper/1810.02513/) (2018)
- [DeceptionNet: Network-Driven Domain Randomization](https://mlsanity.com/paper/1904.02750/) (2019)
- [Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting](https://mlsanity.com/paper/2011.06220/) (2020)
- [Adversarial Variational Optimization of Non-Differentiable Simulators](https://mlsanity.com/paper/1707.07113/) (2017)
- [Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks](https://mlsanity.com/paper/2001.00666/) (2020)

## Built on by (most cited first, 1 of 1)

- [Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching](https://mlsanity.com/paper/2304.07693/) (2023)

## Abstract (arXiv)

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.