# Automatic Data Augmentation for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2010.11695 · HTML: https://mlsanity.com/paper/2010.11695/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 22 of 22)

- [AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation](https://mlsanity.com/paper/2207.13249/) (2022)
- [Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training](https://mlsanity.com/paper/2103.16493/) (2021)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis](https://mlsanity.com/paper/2306.17466/) (2023)
- [ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images](https://mlsanity.com/paper/2307.03273/) (2023)
- [APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation](https://mlsanity.com/paper/2210.01485/) (2022)
- [Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation](https://mlsanity.com/paper/2304.13490/) (2023)
- [Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation](https://mlsanity.com/paper/2305.19084/) (2023)
- [Segmenting Medical Images with Limited Data](https://mlsanity.com/paper/2407.09189/) (2024)
- [A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations](https://mlsanity.com/paper/2207.09869/) (2022)
- [S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2412.13156/) (2024)
- [Deep Anomaly Generation: An Image Translation Approach of Synthesizing Abnormal Banded Chromosome Images](https://mlsanity.com/paper/2109.09702/) (2021)
- [Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture](https://mlsanity.com/paper/2207.10351/) (2022)
- [Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2308.16573/) (2023)
- [Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods](https://mlsanity.com/paper/2403.08352/) (2024)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [Use the Detection Transformer as a Data Augmenter](https://mlsanity.com/paper/2304.04554/) (2023)
- [Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation](https://mlsanity.com/paper/2305.15777/) (2023)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)
- [Challenges in Deep Learning-Based Small Organ Segmentation: A Benchmarking Perspective for Medical Research with Limited Datasets](https://mlsanity.com/paper/2509.05892/) (2025)

## Abstract (arXiv)

Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting; 2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. However, most data augmentation implementations are hand-crafted and suboptimal in medical image processing. To fully exploit the potential of data augmentation, we propose an efficient algorithm to automatically search for the optimal augmentation strategies. We formulate the coupled optimization w.r.t. network weights and augmentation parameters into a differentiable form by means of stochastic relaxation. This formulation allows us to apply alternative gradient-based methods to solve it, i.e. stochastic natural gradient method with adaptive step-size. To the best of our knowledge, it is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models.