Paper Info
Title
Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
Abstract
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 retinal degenerative pathologies. In this work, we propose a robust disease detection architecture with three learning heads, i) A supervised encoder for retinal disease classification, ii) An unsupervised decoder for the reconstruction of disease-specific spatial information, and iii) A novel representation learning module for learning the similarity between encoder-decoder feature and enhancing the accuracy of the model. Our experimental results on two publicly available OCT datasets illustrate that the proposed model outperforms existing state-ofthe-art models in terms of accuracy, interpretability, and robustness for out-of-distribution retinal disease detection.
Year
DOI
Venue
2022
10.1007/978-3-031-16525-2_3
OPHTHALMIC MEDICAL IMAGE ANALYSIS, OMIA 2022
Keywords
DocType
Volume
Retinal degeneration, SD-OCT, Deep learning, Optical coherence tomography, Representation learning
Conference
13576
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Sharif Amit Kamran101.01
Khondker Fariha Hossain222.06
Alireza Tavakkoli300.34
Stewart Lee Zuckerbrod421.72
Salah A. Baker500.34