2019 arXiv
Going Deep in Medical Image Analysis:Concepts, Methods, Challenges and Future Directions
医学图像分析主要包含的模式识别任务是 检测/定位、分割、配准、分类。常见的医学影像包括 Brain、Breast、Eye、Chest、Abdomen等。
青光眼视神经病变 ScienceDirect paper
Li等[68]最近采用了深度转移学习方法,该方法可以很好地调整在ImageNet [69]数据集上预训练的VGG-16模型[31]。为了检测和分类眼中年龄相关性黄斑变性(AMD)和糖尿病性黄斑水肿(DME)疾病,他们使用207,130视网膜光学相干断层扫描(OCT)图像。该方法在视网膜图像中实现了98.6%的预测检测精度,100%。
Ambramoff等[70]使用基于CNN的技术检测眼底图像中的糖尿病视网膜病变(DR)。他们使用公共数据集[71]在他们的研究中评估了设备IDx-DRX2.1,并获得了0.98的AUC分数。
Schlegl等[72]采用深度学习方法检测视网膜图像中的视网膜内囊样液(IRC)和视网膜下液(SRF)。他们采用自动编码器-解码器形成CNN,并使用1,200个OCT视网膜图像进行实验,SRF的AUC为0.92,IRC的AUC为0.94。
Li等[75]基于Inception架构[43]训练了一个深度学习模型,用于识别视网膜图像中的青光眼视神经病变(GON)。他们的模型达到了0.986的AUC,用于区分健康眼睛和GON眼睛。
Christopher等[76]也使用VGG16,Inception v3和ResNet50模型进行转移学习来识别GON。他们使用预先训练的ImageNet模型。对于他们的实验,他们使用14,822个光学神经头(ONH)眼底图像的GON或健康的眼睛。据报道,在眼睛中鉴定中度至重度GON的最佳性能为AUC值0.97,灵敏度为90%,特异性为93%。 Khojasteh等[77]在DIARETDB1[78]和e-Ophtha[79]数据集上使用预先训练的ResNet-50检测视网膜图像中的出口。他们报告的准确度为98%,使用数据检测灵敏度为99%。
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