# Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images

arXiv: https://arxiv.org/abs/2403.09828 · HTML: https://mlsanity.com/paper/2403.09828/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis](https://mlsanity.com/paper/2306.17466/) (2023)

## Built on by (most cited first, 4 of 4)

- [ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification](https://mlsanity.com/paper/2509.14830/) (2025)
- [UI-Styler: Ultrasound Image Style Transfer with Class-Aware Prompts for Cross-Device Diagnosis Using a Frozen Black-Box Inference Network](https://mlsanity.com/paper/2511.17155/) (2025)
- [Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification](https://mlsanity.com/paper/2501.03967/) (2025)
- [SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging](https://mlsanity.com/paper/2503.05916/) (2025)

## Abstract (arXiv)

Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains.