Augmentation based unsupervised domain adaptation
This work addresses the issue of expensive data acquisition and annotation in clinical scenarios, offering an incremental improvement for medical image analysis.
The paper tackles the problem of deep learning models failing to generalize in medical image segmentation due to limited and unrepresentative training data, proposing a domain adaptation method that improves generalization, particularly in challenging cases, as demonstrated on white matter hyperintensities datasets.
The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize. Because in realistic clinical scenarios, data acquisition and annotation is expensive, deep learning models trained on small and unrepresentative data tend to outperform when deployed in data that differs from the one used for training (e.g data from different scanners). In this work, we proposed a domain adaptation methodology to alleviate this problem in segmentation models. Our approach takes advantage of the properties of adversarial domain adaptation and consistency training to achieve more robust adaptation. Using two datasets with white matter hyperintensities (WMH) annotations, we demonstrated that the proposed method improves model generalization even in corner cases where individual strategies tend to fail.