Work

Erasing and Refining Discriminative Features for CNV Subtype Classification in OCT Images

Year: 2026

Type: article

Abstract: Choroidal neovascularization (CNV) subtype classification from optical coherence tomography (OCT) images is clinically important because treatment response and disease prognosis vary across subtypes. However, automated classification remains challenging owing to subtle inter-class differences and considerable imaging noise. We propose an erasing-refining discriminative feature network (ERDF-Net) that mitigates noisy dominant activations and reveals fine-grained structural cues. The model perturbs salient regions to facilitate subtle feature learning, restores clean salient information, and fuses both representations via channel-spatial attention to form a coherent and discriminative embedding of CNV morphology. Experiments on a clinical OCT dataset show that ERDF-Net consistently surpasses state-of-the-art fine-grained and erasing-based methods across multiple metrics. Ablation and visualization analyses further confirm the benefit and interpretability of controlled salient suppression and refined feature fusion. ERDF-Net provides an effective and reliable solution for fine-grained medical image classification.

Source: Journal of Imaging

DOI: 10.3390/jimaging12090405

Authors: Jiayi Zhang, Qingbo Wang, Jiqiang Liu, Aixi Qu

Institutions: Beijing Jiaotong University, Shandong Institute for Product Quality Inspection, Shandong Jianzhu University

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,172