Deep learning algorithms are one of most rapid developing fields into the modern computation technologies. One of the bottlenecks into the implementation of such advaced algorithms is their requirement for a large amount of manually-labelled data for training. For the general-purpose tasks, such as general purpose image classification/detection the huge images datasets are already labelled and collected. For more subject specific tasks (such as electron microscopy images treatment), no labelled data available. Here I demonstrate that a deep learning network can be successfully trained for nanoparticles detection using semi-synthetic data. The real SEM images were used as a textures for rendered nanoparticles at the surface. Training of RetinaNet architecture using transfer learning can be helpful for the large-scale particle distribution analysis. Beyond such applications, the presented approach might be applicable to other tasks, such as image segmentation.

中文翻译：

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扫描电子显微镜的深度学习：纳米粒子检测的合成数据

深度学习算法是现代计算技术发展最快的领域之一。实施此类高级算法的瓶颈之一是它们需要大量手动标记的数据进行训练。对于通用任务，例如通用图像分类/检测，巨大的图像数据集已经被标记和收集。对于更多特定主题的任务（例如电子显微镜图像处理），没有可用的标记数据。在这里，我展示了可以使用半合成数据成功训练深度学习网络进行纳米粒子检测。真实的 SEM 图像被用作表面渲染纳米粒子的纹理。使用迁移学习训练 RetinaNet 架构有助于大规模粒子分布分析。