# LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2308.09026 · HTML: https://mlsanity.com/paper/2308.09026/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions](https://mlsanity.com/paper/2104.03829/) (2021)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [Skin Lesion Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1902.03253/) (2019)
- [A Structural Causal Model for MR Images of Multiple Sclerosis](https://mlsanity.com/paper/2103.03158/) (2021)
- [Pseudo-healthy synthesis with pathology disentanglement and adversarial learning](https://mlsanity.com/paper/2005.01607/) (2020)
- [InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation](https://mlsanity.com/paper/2206.15134/) (2022)
- [Free-form tumor synthesis in computed tomography images via richer generative adversarial network](https://mlsanity.com/paper/2104.09701/) (2021)
- [Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks](https://mlsanity.com/paper/1805.11291/) (2018)
- [Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET](https://mlsanity.com/paper/1901.05733/) (2019)
- [Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network](https://mlsanity.com/paper/1910.11960/) (2019)
- [ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation](https://mlsanity.com/paper/2102.00221/) (2021)
- [Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images](https://mlsanity.com/paper/2208.02135/) (2022)
- [Adnexal Mass Segmentation with Ultrasound Data Synthesis](https://mlsanity.com/paper/2209.12305/) (2022)

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

- [A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts](https://mlsanity.com/paper/2405.10246/) (2024)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [Synthetic Data for Robust Stroke Segmentation](https://mlsanity.com/paper/2404.01946/) (2024)
- [SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints](https://mlsanity.com/paper/2410.01766/) (2024)
- [SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation](https://mlsanity.com/paper/2412.10946/) (2024)
- [HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2511.17614/) (2025)
- [MedDiffuseMix: Preserving Diagnostic Evidence with Saliency-Aware Diffusion Medical Image Data Augmentation](https://mlsanity.com/paper/2606.28419/) (2026)

## Abstract (arXiv)

Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training images. They are often designed at the image level, augmenting the full image, and do not pay attention to specific abnormalities within the image. Here, we present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution, and allowing both lesion populating and inpainting. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation, outperforming several recent Mix-based data augmentation methods. The code will be released at https://github.com/dogabasaran/lesionmix.