FakET：使用神经风格迁移模拟冷冻电子断层扫描图
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
AI总结：
本文提出基于神经风格迁移的FakET方法，用于模拟冷冻透射电镜前向算子并生成训练数据，在保持定位分类性能的同时提速750倍、内存降至1/33。
摘要：
在冷冻电子显微镜中，准确的颗粒定位和分类至关重要。近期的深度学习解决方案虽然取得了成功，但需要大量训练数据集。通常用于生成这些数据集的基于物理的模型生成时间漫长，限制了其广泛适用性。我们提出了 FakET，这是一种基于神经风格迁移的方法，能够模拟任何冷冻透射电子显微镜的前向算子。它可用于根据参考数据调整合成训练数据集，从而生成高质量的模拟显微图像或倾斜序列。为评估所生成数据的质量，我们使用这些数据训练了一个最先进的定位与分类架构，并将其性能与在基准数据上训练的对应模型进行比较。值得注意的是，我们的技术在性能上与之相当，同时将数据生成速度提高750倍，内存占用减少31/33（即减少至1/33），并能很好地扩展到典型的透射电子显微镜探测器尺寸。该方法利用了GPU加速和并行处理。源代码可在 https://github.com/paloha/faket 获取。
英文摘要：
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training data sets. The protracted generation time of physics-based models, often employed to produce these data sets, limits their broad applicability. We introduce FakET, a method based on Neural Style Transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training data set according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed 750 times, uses 33 times less memory, and scales well to typical transmission electron microscope detector sizes. It leverages GPU acceleration and parallel processing. The source code is available at https://github.com/paloha/faket.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
