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Efficient pyramid channel attention network for pathological myopia recognition with pretraining-and-finetuning
Xiaoqing Zhang1, Jilu Zhao2, Yan Li3
1Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
This study introduces EPCA-Net, an efficient deep learning model for recognizing pathological myopia (PM) using fundus images. EPCA-Net leverages clinical pathology distribution priors to achieve high accuracy in diagnosing this leading cause of vision impairment.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Pathological myopia (PM) is a primary cause of vision impairment globally.
- Current deep learning models for PM often overlook crucial pathology distribution priors found in fundus images.
- Effective PM diagnosis relies on understanding global-local pathology patterns.
Purpose of the Study:
- To develop an efficient deep learning model that incorporates clinical pathology distribution priors for pathological myopia recognition.
- To introduce the Efficient Pyramid Channel Attention (EPCA) module and EPCA-Net for enhanced PM detection.
- To evaluate the efficacy of a pretraining-and-finetuning paradigm for PM recognition using limited medical data.
Main Methods:
- Proposed an Efficient Pyramid Channel Attention (EPCA) module integrating pyramid pooling and multi-scale context fusion.
- Constructed EPCA-Net by stacking EPCA modules for automatic PM recognition from fundus images.
- Adapted pre-trained natural image models using EPCA as adapters within a pretraining-and-finetuning framework.
- Created the PM-fundus benchmark dataset for evaluating PM recognition models.
Main Results:
- EPCA-Net achieved 97.56% accuracy on the PM-fundus dataset, outperforming existing state-of-the-art methods.
- The pretraining-and-finetuning approach with EPCA demonstrated competitive performance with fewer tunable parameters.
- The proposed method shows potential for leveraging large natural image models in data-limited medical scenarios.
Conclusions:
- EPCA-Net effectively utilizes pathological myopia distribution priors, offering superior performance in automated fundus image-based recognition.
- The pretraining-and-finetuning strategy with attention modules presents a promising direction for medical image analysis with limited datasets.
- This work provides a robust and efficient tool for pathological myopia diagnosis, aiding in the management of vision impairment.
