# Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

arXiv: https://arxiv.org/abs/2002.09703 · HTML: https://mlsanity.com/paper/2002.09703/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2206.14302/) (2022)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Data Optimization in Deep Learning: A Survey](https://mlsanity.com/paper/2310.16499/) (2023)
- [MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis](https://mlsanity.com/paper/2306.17466/) (2023)
- [Segmenting Medical Images with Limited Data](https://mlsanity.com/paper/2407.09189/) (2024)
- [S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2412.13156/) (2024)
- [Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2610.18133/) (2026)

## Abstract (arXiv)

Conventional data augmentation realized by performing simple pre-processing operations (eg, rotation, crop, etc) has been validated for its advantage in enhancing the performance for medical image segmentation. However, the data generated by these conventional augmentation methods are random and sometimes harmful to the subsequent segmentation. In this paper, we developed a novel automatic learning-based data augmentation method for medical image segmentation which models the augmentation task as a trial-and-error procedure using deep reinforcement learning (DRL). In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. Specifically, the best sequential combination of different basic operations is automatically learned by directly maximizing the performance improvement (ie, Dice ratio) on the available validation set. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.