Domain Adaptation-Based Deep Learning Models for Forecasting and
Diagnosis of Glaucoma Disease
- Yeganeh Madadi ,
- Hashem Abu-Serhan ,
- Siamak Yousefi
Yeganeh Madadi
University of Tennessee Health Science Center, University of Tennessee Health Science Center
Corresponding Author:[email protected]
Author ProfileAbstract
Domain adaptation methods are designed to extract shared
domain-invariant features by projecting data on a common subspace in
order to align their domain distributions. However, these methods do not
usually consider domain-specific features, and therefore their
distributions may not be well aligned. To address this problem, we
introduce a novel model that learns domain-invariant and domain-specific
representations to extract both their general and specific features. We
also propose progressive weighting to accurately transfer the source
domain knowledge and to mitigate negative knowledge transfer from the
source to the target domain and to employ low-rank coding for aligning
the source and target distributions. We evaluated the model based on
several real-world datasets and showed that our method significantly
improved the accuracy for forecasting and diagnosis of glaucoma disease
from fundus photographs.