# DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem

arXiv: https://arxiv.org/abs/2303.05912 · HTML: https://mlsanity.com/paper/2303.05912/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 91 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 30 of 42)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [GridMask Data Augmentation](https://mlsanity.com/paper/2001.04086/) (2020)
- [Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/1901.08211/) (2019)
- [MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset](https://mlsanity.com/paper/2005.06465/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020)
- [RegNet: Self-Regulated Network for Image Classification](https://mlsanity.com/paper/2101.00590/) (2021)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [Content-Aware GAN Compression](https://mlsanity.com/paper/2104.02244/) (2021)
- [Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images](https://mlsanity.com/paper/2004.05645/) (2020)
- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [Analysis of Hand Segmentation in the Wild](https://mlsanity.com/paper/1803.03317/) (2018)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio](https://mlsanity.com/paper/1807.03434/) (2018)
- [Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach](https://mlsanity.com/paper/2009.10181/) (2020)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net](https://mlsanity.com/paper/2007.04774/) (2020)
- [Mind the Gap in Distilling StyleGANs](https://mlsanity.com/paper/2208.08840/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation](https://mlsanity.com/paper/2608.29944/) (2026)
- [Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations](https://mlsanity.com/paper/2609.02600/) (2026)

## Abstract (arXiv)

Due to the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of semantic segmentation of Computed Tomography (CT) images have become highly desirable. In this work, we present a deeper analysis of how data augmentation techniques improve segmentation performance on this problem. We evaluate 20 traditional augmentation techniques on five public datasets. Six different probabilities of applying each augmentation technique on an image were evaluated. We also assess a different training methodology where the training subsets are combined into a single larger set. All networks were evaluated through a 5-fold cross-validation strategy, resulting in over 4,600 experiments. We also propose a novel data augmentation technique based on Generative Adversarial Networks (GANs) to create new healthy and unhealthy lung CT images, evaluating four variations of our approach with the same six probabilities of the traditional methods. Our findings show that GAN-based techniques and spatial-level transformations are the most promising for improving the learning of deep models on this problem, with the StarGANv2 + F with a probability of 0.3 achieving the highest F-score value on the Ricord1a dataset in the unified training strategy. Our code is publicly available at https://github.com/VRI-UFPR/DACov2022