Authors
Zhiming Xiong, Meiting Yu, Tong Lv, Ruixuan Xu, Chongjun Huang, Guohui Yuan, Zhuoran Wang
Published in
Frontiers in medicine. Volume 13. Pages 1879177. Epub Aug 17, 2026.
Abstract
Fundus diseases are a leading cause of irreversible blindness worldwide. Existing Color Fundus Photography (CFP) and Optical Coherence Tomography (OCT) fusion methods face two major challenges: feature noise interference and the semantic gap between heterogeneous modalities, leading to suboptimal fusion and limited diagnostic accuracy. To address these issues, this paper proposes a Multi-modal Hybrid Fusion Network (MHF-Net), which is designed to suppress noise, enhance intra-modal feature representation, and explicitly establish cross-modal semantic correlations. The proposed framework consists of three core components: a heterogeneous dual-stream encoder to extract multi-level pathological features from both CFP and OCT; a Multi-scale Feature Enhancement (MFE) module to reduce background noise while reinforcing intra-modal features; and a Hierarchical Contextual Fusion (HCF) module to explicitly establish deep semantic correlations across modalities and scales. Evaluated on the Topcon-MM dataset, MHF-Net achieves a Macro F1 of 80.91%, a Macro Recall of 76.80%, and a Sample Accuracy of 67.32%, demonstrating competitive performance against state-of-the-art multimodal models and recent vision foundation model baselines. This study demonstrates that explicit hierarchical fusion with multi-scale feature enhancement effectively resolves the semantic gap between CFP and OCT modalities, providing a robust and clinically promising solution for automated multimodal diagnosis of complex fundus diseases.
PMID:
42676350
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.
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