Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images. (arXiv:2206.12136v1 [eess.IV])

Ophthalmic images may contain identical-looking pathologies that can cause
failure in automated techniques to distinguish different retinal degenerative
diseases. Additionally, reliance on large annotated datasets and lack of
knowledge distillation can restrict ML-based clinical support systems'
deployment in real-world environments. To improve the robustness and
transferability of knowledge, an enhanced feature-learning module is required
to extract meaningful spatial representations from the retinal subspace. Such a
module, if used effectively, can detect unique disease traits and differentiate
the severity of such …

arxiv detection disease feature images learning representation representation learning