# Synthetic Sample Selection via Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.11331 · HTML: https://mlsanity.com/paper/2008.11331/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Synthetic Augmentation with Large-scale Unconditional Pre-training](https://mlsanity.com/paper/2308.04020/) (2023)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Synthetic Sample Selection for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2304.02846/) (2023)
- [SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation](https://mlsanity.com/paper/2112.02862/) (2021)
- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01737/) (2022)
- [Realistic Data Enrichment for Robust Image Segmentation in Histopathology](https://mlsanity.com/paper/2304.09534/) (2023)
- [Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification](https://mlsanity.com/paper/2203.07815/) (2022)
- [Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets](https://mlsanity.com/paper/2503.09221/) (2025)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)
- [Graph Conditioned Diffusion for Controllable Histopathology Image Generation](https://mlsanity.com/paper/2510.07129/) (2025)

## Abstract (arXiv)

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.