Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
This addresses the challenge of adopting machine learning models in clinical practice by improving robustness to domain shifts in medical imaging, though it appears incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of deep learning models in medical imaging struggling to generalize due to data heterogeneity, proposing an unsupervised domain adaptation method for brain MRI segmentation that achieves high accuracy and surpasses state-of-the-art performance in most cases.
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a significant challenge in adopting machine learning models for clinical practice. We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques. To evaluate the effectiveness of our method, we conduct extensive experiments across diverse datasets, modalities, and segmentation tasks, comparing against the state-of-the-art methods. The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks, surpassing the state-of-the-art performance in the majority of cases.