psf的matlab代码-Localization:本土化
psf的matlab代码本土化
这是通过在Matlab（Matconvnet）和Python（Keras和TensorFlow）上进行深度学习而实现的三维本地化显微镜的实现。
该模型基于深度卷积神经网络（CNN），可从常规宽视野荧光显微镜捕获的单个2D图像中检索荧光团的3D位置。
具有挑战性的3D定位通过两个级联的CNN转换为多标签分类问题。
该存储库包括：
珠子/细胞/颗粒定位培训和测试的源代码。
模拟训练数据集的源代码。
斑马鱼血液移动的数据集通过宽视野荧光显微镜收集，用于细胞定位/跟踪。
在模拟/收集的数据集上进行训练和测试的示例。
该代码已记录并设计为易于扩展。
如果您在研究中使用它，请考虑引用该存储库（下面的bibtex）。
入门
斑马鱼或系统PSF的数据集可从Google云端硬盘获取。
对于Python
3.5.5用户，请同时安装和。
对于Matlab用户，请安装。
训练级联的CNN
train_network_A.py(.m)提供了一个训练第一个CNN（横向检测CNN）以确定每个贴片的中心横向位置是否存在衍射图样的示例。
train_network_B.py(.m)提供了
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资源详情
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