AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering
This addresses the problem of AMD severity assessment for medical diagnosis, but it is incremental as it builds on existing methods with added explainability.
The paper tackles predicting severity of age-related macular degeneration (AMD) from OCT images using deep learning-based image registration and clustering, achieving disease classification performance that matches state-of-the-art methods and good severity prediction on unseen data.
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding label and severity decisions