Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
This work addresses the challenge of limited labeled data in medical imaging by improving classification accuracy for ophthalmic images, though it is incremental as it builds on existing domain adaptation techniques.
The authors tackled the problem of domain adaptation for ophthalmic image classification by proposing SELDA, a stacking ensemble method that combines three base domain adaptation models, achieving high accuracy on the AREDS benchmark dataset.
Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.