# Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis

arXiv: https://arxiv.org/abs/2303.01178 · HTML: https://mlsanity.com/paper/2303.01178/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 74 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning](https://mlsanity.com/paper/2007.07936/) (2020)
- [Data Augmentation Can Improve Robustness](https://mlsanity.com/paper/2111.05328/) (2021)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness](https://mlsanity.com/paper/2010.08001/) (2020)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment](https://mlsanity.com/paper/1907.09085/) (2019)
- [Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation](https://mlsanity.com/paper/2110.07858/) (2021)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [Data Augmentation for Skin Lesion Analysis](https://mlsanity.com/paper/1809.01442/) (2018)
- [Multi-View Image Generation from a Single-View](https://mlsanity.com/paper/1704.04886/) (2017)
- [Automatic Data Augmentation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2010.11695/) (2020)
- [Assessment of algorithms for mitosis detection in breast cancer histopathology images](https://mlsanity.com/paper/1411.5825/) (2014)
- [Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection](https://mlsanity.com/paper/1901.04619/) (2019)
- [Robust Retinal Vessel Segmentation from a Data Augmentation Perspective](https://mlsanity.com/paper/2007.15883/) (2020)
- [Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19](https://mlsanity.com/paper/2012.03663/) (2020)
- [Classification of breast cancer histology images using transfer learning](https://mlsanity.com/paper/1802.09424/) (2018)
- [Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images](https://mlsanity.com/paper/2009.01181/) (2020)
- [Principled Ultrasound Data Augmentation for Classification of Standard Planes](https://mlsanity.com/paper/2103.07895/) (2021)
- [A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2006.01693/) (2020)
- [Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis](https://mlsanity.com/paper/2009.12610/) (2020)
- [Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation](https://mlsanity.com/paper/2101.11135/) (2021)

## Built on by (most cited first, 7 of 7)

- [Improving text-conditioned latent diffusion for cancer pathology](https://mlsanity.com/paper/2412.06487/) (2024)
- [NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation](https://mlsanity.com/paper/2410.16671/) (2024)
- [Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours](https://mlsanity.com/paper/2506.13292/) (2025)
- [Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm](https://mlsanity.com/paper/2402.09658/) (2024)
- [pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation](https://mlsanity.com/paper/2505.24421/) (2025)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)
- [Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels](https://mlsanity.com/paper/2511.18894/) (2025)

## Abstract (arXiv)

In the realm of medical imaging, the training of machine learning models necessitates a large and varied training dataset to ensure robustness and interoperability. However, acquiring such diverse and heterogeneous data can be difficult due to the need for expert labeling of each image and privacy concerns associated with medical data. To circumvent these challenges, data augmentation has emerged as a promising and cost-effective technique for increasing the size and diversity of the training dataset. In this study, we provide a comprehensive review of the specific data augmentation techniques employed in medical imaging and explore their benefits. We conducted an in-depth study of all data augmentation techniques used in medical imaging, identifying 11 different purposes and collecting 65 distinct techniques. The techniques were operationalized into spatial transformation-based, color and contrast adjustment-based, noise-based, deformation-based, data mixing-based, filters and mask-based, division-based, multi-scale and multi-view-based, and meta-learning-based categories. We observed that some techniques require manual specification of all parameters, while others rely on automation to adjust the type and magnitude of augmentation based on task requirements. The utilization of these techniques enables the development of more robust models that can be applied in domains with limited or challenging data availability. It is expected that the list of available techniques will expand in the future, providing researchers with additional options to consider.