【医学影像系列：二】2019 综述阅读 Going Deep in Medical Image Analysis:Concepts, Methods, Challenges and Future

2019 arXiv

Going Deep in Medical Image Analysis:Concepts, Methods, Challenges and Future Directions

Method

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。

• 检测/定位

• 分割

• 配准

• 分类

Eye

GON

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％。

Datasets

Future

• 小数据问题：迁移学习、数据增广、GAN样本生成
• 图像结合病例资料的多模态学习
• 参考计算机视觉、机器学习领域的新工作

References

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