AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering
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
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringImage Registrationseverity predictionSimilar Papers 제목 키워드 기반
Explainable unsupervised multi-modal image registration using deep networks
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims to geometrically 'pair' diagnoses from …
Decision Makingimage-classificationImage ClassificationImage RegistrationDrivenMorph: Bridging Attention Mechanism and Variational Image Registration via Difference Modeling
Medical image registration benefits significantly from deep learning, yet existing approaches often lack physical explainability and fine-grained deformation control. Motivated by Demons algorithms, we propose a novel Dr…
Medical Image RegistrationTowards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment
Dysarthria severity assessment is essential for therapy planning and longitudinal monitoring, yet manual perceptual rating is time-consuming and variable across clinicians. Although deep learning models achieve strong pe…
Feature ImportanceWhich one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments
The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxic…
regressionHuman-AI Collaboration and Explainability for 2D/3D Registration Quality Assurance
Purpose: As surgery increasingly integrates advanced imaging, algorithms, and robotics to automate complex tasks, human judgment of system correctness remains a vital safeguard for patient safety. A critical example is 2…