MediAug: Exploring Visual Augmentation in Medical Imaging

Abstract: Data augmentation is essential in medical imaging for improving classification accuracy, lesion detection, and organ segmentation under limited data conditions. However, two significant challenges remain. First, a pronounced domain gap between natural photographs and medical images can distort critical disease features. Second, augmentation studies in medical imaging are fragmented and limited to single tasks or architectures, leaving the benefits of advanced mix-based strategies unclear. To address these challenges, we propose a unified evaluation framework with six mix-based augmentation methods integrated with both convolutional and transformer backbones on brain tumour MRI and eye disease fundus datasets. Our contributions are threefold. (1) We introduce MediAug, a comprehensive and reproducible benchmark for advanced data augmentation in medical imaging. (2) We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix with ResNet-50 and ViT-B backbones. (3) We demonstrate through extensive experiments that MixUp yields the greatest improvement on the brain tumor classification task for ResNet-50 with 79.19% accuracy and SnapMix yields the greatest improvement for ViT-B with 99.44% accuracy, and that YOCO yields the greatest improvement on the eye disease classification task for ResNet-50 with 91.60% accuracy and CutMix yields the greatest improvement for ViT-B with 97.94% accuracy. Code will be available at https://github.com/AIGeeksGroup/MediAug.

MediAug: Exploring Visual Augmentation in Medical Imaging

"MediAug: Exploring Visual Augmentation in Medical Imaging" addresses critical challenges in the application of data augmentation (DA) techniques to medical imaging, a domain where they have seen less exploration compared to natural image tasks. The paper introduces MediAug, a comprehensive benchmark and evaluation framework for six mix-based DA methods on brain tumour MRI and eye disease fundus datasets, leveraging both convolutional and transformer backbone architectures.

Key Contributions and Findings

The paper enumerates three primary contributions:

1. Benchmark Development: The introduction of MediAug, a reproducible benchmark that facilitates the systematic evaluation of data augmentation strategies specifically in medical imaging. This serves as a significant resource for researchers, enabling consistent comparisons across different methods.

2. Systematic Evaluation: A thorough evaluation of six prominent mix-based augmentation techniques—MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix. These were assessed using two backbone architectures, ResNet-50 and ViT-B, to determine their efficacy in enhancing model performance on two distinct medical imaging datasets.

3. Performance Insights: The evaluation yielded strong numerical results, identifying MixUp as optimal for brain tumour classification with ResNet-50, achieving an accuracy of 79.19%. SnapMix also excelled with ViT-B on the same task, reaching 99.44% accuracy. For eye disease classification, YOCO with ResNet-50 and CutMix with ViT-B achieved 91.60% and 97.94% accuracy, respectively. These findings are particularly notable as they provide specific guidance on preferred combinations of augmentation methods and architectures for targeted medical imaging tasks.

Implications and Future Directions

The paper’s findings emphasize the importance of tailored augmentation strategies in medical imaging, recognizing distinct optimal techniques for brain tumour and eye disease classification tasks. These insights are practically significant, guiding the deployment of DA methods in clinical AI systems to ensure robust diagnosis and classification under scarce data conditions.

Additionally, the successful application of transformer architectures (specifically ViT-B) in conjunction with mix-based augmentation strategies suggests potential avenues for leveraging transformers’ attention mechanisms to further enhance feature extraction in complex medical images. This can motivate further exploration of how such architectures might be fine-tuned or adapted for various medical imaging modalities beyond those currently tested.

Finally, the comprehensive ablation study on CutMix’s interpolation parameter underscores the necessity for detailed hyperparameter tuning in mix-based data augmentation strategies. This highlights the broader need for systematic optimization across diverse methods to get the most robust and reliable model performance in real-world scenarios.

Conclusion

The study offers a valuable resource for the medical imaging research community by proposing MediAug as a standardized framework for evaluating advanced data augmentation techniques. By identifying optimal combinations of augmentation methods and architectures for specific medical imaging tasks, it provides actionable insights that can accelerate the development and integration of AI into clinical practice. Importantly, it also lays the groundwork for future research into adapting and optimizing transformers in medical imaging applications, suggesting that these findings have the potential to influence future advancements in AI for healthcare. Overall, MediAug stands as a pivotal step in bridging the domain gap between natural and medical images, offering a pathway toward more generalizable and effective clinical AI systems.