ArticleBiosensors2022
HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Frequency-Aware Self-Supervised Framework for MEMS-OCT Denoising.Biosensors · 2026Article
- SLight-Net: a lightweight spectral-layer aware network for retinal disease detection based on optical coherence tomography.Frontiers in cell and developmental biology · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- [Research on attention-enhanced networks for subtype classification of age-related macular degeneration in optical coherence tomography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- HyReti-Net: hybrid retinal diseases classification and diagnosis network using optical coherence tomography.Frontiers in medicine · 2025Article
- L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.Biomedical engineering letters · 2024Article
- Multiscale attention-over-attention network for retinal disease recognition in OCT radiology images.Frontiers in medicine · 2024Article
- Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT.Sensors (Basel, Switzerland) · 2023Article
- Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images.Diagnostics (Basel, Switzerland) · 2023Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
- Vision transformers: The next frontier for deep learning-based ophthalmic image analysis.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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Authors and funding
6 authors.
Funding
Abstract
Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal disease. In this study, we propose a hybrid ConvNet-Transformer network (HCTNet) and verify the feasibility of a Transformer-based method for retinal OCT image classification. The HCTNet first utilizes a low-level feature extraction module based on the residual dense block to generate low-level features for facilitating the network training. Then, two parallel branches of the Transformer and the ConvNet are designed to exploit the global and local context of the OCT images. Finally, a feature fusion module based on an adaptive re-weighting mechanism is employed to combine the extracted global and local features for predicting the category of OCT images in the testing datasets. The HCTNet combines the advantage of the convolutional neural network in extracting local features and the advantage of the vision Transformer in establishing long-range dependencies. A verification on two public retinal OCT datasets shows that our HCTNet method achieves an overall accuracy of 91.56% and 86.18%, respectively, outperforming the pure ViT and several ConvNet-based classification methods.
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