Glaucoma is an irreversible ocular disease and is the second leading cause of
visual disability worldwide. Slow vision loss and the asymptomatic nature of
the disease make its diagnosis challenging. Early detection is crucial for
preventing irreversible blindness. Ophthalmologists primarily use retinal
fundus images as a non-invasive screening method. Convolutional neural networks
(CNN) have demonstrated high accuracy in the classification of medical images.
Nevertheless, CNN's translation-invariant nature and inability to handle the
part-whole relationship between objects make its direct application unsuitable
for glaucomatous fundus image classification, as it requires a large number of
labelled images for training. This work reviews existing state of the art
models and proposes InceptionCaps, a novel capsule network (CapsNet) based deep
learning model having pre-trained InceptionV3 as its convolution base, for
automatic glaucoma classification. InceptionCaps achieved an accuracy of 0.956,
specificity of 0.96, and AUC of 0.9556, which surpasses several
state-of-the-art deep learning model performances on the RIM-ONE v2 dataset.
The obtained result demonstrates the robustness of the proposed deep learning
model.

一项关于深度学习模型在自动青光眼分类中的研究，采用 InceptionCaps 作为基于预训练 InceptionV3 的胶囊网络，通过对视网膜眼底图像的分类，取得了较高的准确性和鲁棒性。

InceptionCaps: 数据稀缺环境下高效的青光眼分类模型

InceptionCaps: A Performant Glaucoma Classification Model for  Data-scarce Environment

Pathological myopia (PM) is the leading ocular disease for impaired vision
and blindness worldwide. The key to detecting PM as early as possible is to
detect informative features in global and local lesion regions, such as fundus
tessellation, atrophy and maculopathy. However, applying classical
convolutional neural networks (CNNs) to efficiently highlight global and local
lesion context information in feature maps is quite challenging. To tackle this
issue, we aim to fully leverage the potential of global and local lesion
information with attention module design. Based on this, we propose an
efficient pyramid channel attention (EPCA) module, which dynamically explores
the relative importance of global and local lesion context information in
feature maps. Then we combine the EPCA module with the backbone network to
construct EPCA-Net for automatic PM detection based on fundus images. In
addition, we construct a PM dataset termed PM-fundus by collecting fundus
images of PM from publicly available datasets (e.g., the PALM dataset and ODIR
dataset). The comprehensive experiments are conducted on three datasets,
demonstrating that our EPCA-Net outperforms state-of-the-art methods in
detecting PM. Furthermore, motivated by the recent pretraining-and-finetuning
paradigm, we attempt to adapt pre-trained natural image models for PM detection
by freezing them and treating the EPCA module and other attention modules as
the adapters. The results show that our method with the
pretraining-and-finetuning paradigm achieves competitive performance through
comparisons to part of methods with traditional fine-tuning methods with fewer
tunable parameters.

我们提出了一个使用注意力模块的有效金字塔通道注意力（EPCA）网络，用于基于眼底图像自动检测病理性近视（PM）。在三个数据集上的实验表明，我们的 EPCA-Net 在检测 PM 方面优于现有方法。此外，我们尝试了将预训练的自然图像模型适应为 PM 检测的方法，并通过与传统的微调方法相比较，在具有较少可调参数的情况下取得了有竞争力的性能。

高效金字塔通道注意力网络用于病理性近视检测

Efficient Pyramid Channel Attention Network for Pathological Myopia  Detection

According to multiple authoritative authorities, including the World Health
Organization, vision-related impairments and disorders are becoming a
significant issue. According to a recent report, one of the leading causes of
irreversible blindness in persons over the age of 50 is delayed cataract
treatment. A cataract is a cloudy spot in the eye's lens that causes visual
loss. Cataracts often develop slowly and consequently result in difficulty in
driving, reading, and even recognizing faces. This necessitates the development
of rapid and dependable diagnosis and treatment solutions for ocular illnesses.
Previously, such visual illness diagnosis were done manually, which was
time-consuming and prone to human mistake. However, as technology advances,
automated, computer-based methods that decrease both time and human labor while
producing trustworthy results are now accessible. In this study, we developed a
CNN-LSTM-based model architecture with the goal of creating a low-cost
diagnostic system that can classify normal and cataractous cases of ocular
disease from fundus images. The proposed model was trained on the publicly
available ODIR dataset, which included fundus images of patients' left and
right eyes. The suggested architecture outperformed previous systems with a
state-of-the-art 97.53% accuracy.

本研究利用 CNN-LSTM 模型结构从眼底图像中诊断出正常或白内障眼病例，该模型在 ODIR 数据集上测试的准确率高达 97.53％，由此有望为眼科疾病的诊断提供快速和可靠的解决方案。