Abnormalities in retinal fundus images may indicate certain pathologies such
as diabetic retinopathy, hypertension, stroke, glaucoma, retinal macular edema,
venous occlusion, and atherosclerosis, making the study and analysis of retinal
images of great significance. In conventional medicine, the diagnosis of
retina-related diseases relies on a physician's subjective assessment of the
retinal fundus images, which is a time-consuming process and the accuracy is
highly dependent on the physician's subjective experience. To this end, this
paper proposes a fast, objective, and accurate method for the diagnosis of
diseases related to retinal fundus images. This method is a multiclassification
study of normal samples and 13 categories of disease samples on the STARE
database, with a test set accuracy of 99.96%. Compared with other studies, our
method achieved the highest accuracy. This study innovatively propose
Segmentation-based Vascular Enhancement(SVE). After comparing the
classification performances of the deep learning models of SVE images, original
images and Smooth Grad-CAM ++ images, we extracted the deep learning features
and traditional features of the SVE images and input them into nine meta
learners for classification. The results shows that our proposed
UNet-SVE-VGG-MLP model has the optimal performance for classifying diseases
related to retinal fundus images on the STARE database, with a overall accuracy
of 99.96% and a weighted AUC of 99.98% for the 14 categories on test dataset.
This method can be used to realize rapid, objective, and accurate
classification and diagnosis of retinal fundus image related diseases.

该论文提出了一种快速、客观、准确的诊断与视网膜底层图像相关疾病的方法，采用多分类研究正常样本和 13 类疾病样本在 STARE 数据库上，测试集准确率达到 99.96%，并与其他研究相比取得了最高准确率。创新地提出 “基于分割的血管增强（SVE）” 方法，经比较深度学习模型在 SVE 图像、原始图像和平滑 Grad-CAM ++ 图像上的分类性能后，提取 SVE 图像的深度学习特征和传统特征并输入到九个元学习器进行分类，结果表明我们提出的 UNet-SVE-VGG-MLP 模型在 STARE 数据库上对与视网膜底层图像相关疾病的分类具有最佳性能，测试集的整体准确率达到 99.96%，14 个类别的加权 AUC 为 99.98%。该方法可实现视网膜底层图像相关疾病的快速、客观、准确的分类与诊断。

结合基于分割的血管增强和深度学习特征的一种改进的视网膜疾病诊断方法

A better approach to diagnose retinal diseases: Combining our  Segmentation-based Vascular Enhancement with deep learning features

Driver distractions are known to be the dominant cause of road accidents.
While monitoring systems can detect non-driving-related activities and
facilitate reducing the risks, they must be accurate and efficient to be
applicable. Unfortunately, state-of-the-art methods prioritize accuracy while
ignoring latency because they leverage cross-view and multimodal videos in
which consecutive frames are highly similar. Thus, in this paper, we pursue
time-effective detection models by neglecting the temporal relation between
video frames and investigate the importance of each sensing modality in
detecting drives' activities. Experiments demonstrate that 1) our proposed
algorithms are real-time and can achieve similar performances (97.5\% AUC-PR)
with significantly reduced computation compared with video-based models; 2) the
top view with the infrared channel is more informative than any other single
modality. Furthermore, we enhance the DAD dataset by manually annotating its
test set to enable multiclassification. We also thoroughly analyze the
influence of visual sensor types and their placements on the prediction of each
class. The code and the new labels will be released.

该研究提出了一种实时检测驾驶人行为的新方法，通过研究不同的感知模态发现红外视频在检测中更具有信息量，并通过多分类技术扩展 DAD 数据集。