GABNet: global attention block for retinal OCT disease classification.
GABNet: global attention block for retinal OCT disease classification.
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DOI：
10.3389/fnins.2023.1143422
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发表时间：
2023
影响因子：
4.3
通讯作者：
Zeng, Fanxin
中科院分区：
文献类型：
--
作者：
Huang, Xuan;Ai, Zhuang;Wang, Hui;She, Chongyang;Feng, Jing;Wei, Qihao;Hao, Baohai;Tao, Yong;Lu, Yaping;Zeng, Fanxin
视网膜是重要的眼部结构。在各种眼科疾病中，视网膜病变由于其高患病率和诱发失明的倾向而引起了相当大的科学兴趣。在眼科采用的临床评价技术中，光学相干断层扫描（OCT）是最常用的，因为它允许无创、快速采集视网膜的高分辨率横截面图像。及时发现和干预可以显著降低失明风险，有效降低全国视力障碍发病率。这项研究为前馈卷积神经网络（CNN）引入了一种新的，高效的全局注意力块（GAB）。GAB为任何中间特征图生成沿沿着三个维度（高度、宽度和通道）的注意力图，然后通过将其与输入特征图相乘来计算自适应特征权重。这个GAB是一个多功能模块，可以与任何CNN无缝集成，显著提高其分类性能。基于GAB，我们提出了一个轻量级的分类网络模型，GABNet，我们开发的UCSD一般视网膜OCT数据集，包括108，312 OCT图像从4686例患者，包括脉络膜新生血管（CNV），糖尿病黄斑水肿（DME），玻璃疣，和正常情况下。值得注意的是，我们的方法比EfficientNetV 2B 3网络模型的分类准确率提高了3.7%。我们进一步采用梯度加权类激活映射（Grad-CAM）来突出每个类的视网膜OCT图像上的感兴趣区域，使医生能够轻松解释模型预测并提高其评估相关模型的效率。随着OCT技术在视网膜图像临床诊断中的使用和应用越来越多，我们的方法提供了一个额外的诊断工具，以提高临床OCT视网膜图像的诊断效率。
The retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments. This study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases. Notably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models. With the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.
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糖尿病性黄斑水肿，视网膜病变和与年龄相关的黄斑变性为炎症状况。
DOI：
10.5114/aoms.2016.61918
发表时间：
2016-10-01
期刊：
Archives of medical science : AMS
影响因子：
--
作者：
Das UN
通讯作者：
Das UN
影响因子：
6.1
作者：
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通讯作者：
Alheyane, Omar
影响因子：
4.8
作者：
Das, N. Narayan;Kumar, N.;Singh, D.
通讯作者：
Singh, D.
影响因子：
3.7
作者：
Li, Xia;Shen, Xi;Li, Tie-Qiang
通讯作者：
Li, Tie-Qiang
影响因子：
10.9
作者：
Liu, Yu-Ying;Chen, Mei;Ishikawa, Hiroshi;Wollstein, Gadi;Schuman, Joel S.;Rehg, James M.
通讯作者：
Rehg, James M.