Difficulty-Aware Pseudo-Label Correction Network for Fine-Grained Classification of Choroidal Neovascularization in OCT Images

Choroidal neovascularization (CNV) classification is a fine-grained classification task. Accurate classification of CNV in optical coherence tomography (OCT) images is crucial for clinical treatment. However, image acquisition noise degrades image quality and exacerbates confirmation bias from class imbalance in medical datasets. Moreover, significant inter-class ambiguity in fine-grained categories can misclassify informative samples (e.g., hard samples or minority class samples) when generating pseudo-labels, leading to sub-optimal classifiers. To address these challenges, we propose a difficulty-aware pseudo-label correction network (DPLC-Net). Specifically, we designed a robust feature mining module using feature similarity loss to maintain consistency between generated adversarial and original samples, enabling noise-resistant feature learning. A difficulty-aware pseudo-label correction module mines and corrects potential noisy pseudo-labels to improve classification performance. Finally, to alleviate data bias and leverage all unlabeled samples, we integrated a hybrid consistency and pseudo-labeling module comprising adaptive weighted consistency loss (AWCL) and class-aware dynamic threshold strategy (CDTS). AWCL adaptively learns weights for unlabeled samples, effectively utilizing all unlabeled data through weighted consistency loss. CDTS dynamically adjusts confidence thresholds based on class distribution and model learning status, improving pseudo-label quantity and quality. Experiments on private and public OCT datasets demonstrate that our method outperforms state-of-the-art methods.