Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
This addresses the challenge of model generalization in medical imaging for clinicians, but it is incremental as it applies an existing UDA technique to a specific domain.
The paper tackles the problem of deep learning models failing to generalize from public datasets to real-world clinical mammogram images due to domain differences, by proposing an unsupervised domain adaptation method using Cycle-GAN, which improves model performance without requiring manual annotations.
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.