  Pathological myopia (PM) is the leading ocular disease for impaired vision
worldwide. Clinically, the characteristic of pathology distribution in PM is
global-local on the fundus image, which plays a significant role in assisting
clinicians in diagnosing PM. However, most existing deep neural networks
focused on designing complex architectures but rarely explored the pathology
distribution prior of PM. To tackle this issue, we propose an efficient pyramid
channel attention (EPCA) module, which fully leverages the potential of the
clinical pathology prior of PM with pyramid pooling and multi-scale context
fusion. Then, we construct EPCA-Net for automatic PM recognition based on
fundus images by stacking a sequence of EPCA modules. Moreover, motivated by
the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained
natural image models for PM recognition by freezing them and treating the EPCA
and other attention modules as adapters. In addition, we construct a PM
recognition benchmark termed PM-fundus by collecting fundus images of PM from
publicly available datasets. The comprehensive experiments demonstrate the
superiority of our EPCA-Net over state-of-the-art methods in the PM recognition
task. The results also show that our method based on the
pretraining-and-finetuning paradigm achieves competitive performance through
comparisons to part of previous methods based on traditional fine-tuning
paradigm with fewer tunable parameters, which has the potential to leverage
more natural image foundation models to address the PM recognition task in
limited medical data regime.
