标题： 条件自编码器的生成建模：构建一个集成细胞 显示英文标题

标题： Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

摘要： 我们提出了一种条件生成模型，用于从显微图像中学习细胞和细胞核形态的变化以及亚细胞结构的位置。 我们的模型适用于广泛的亚细胞定位，并允许从荧光图像中对细胞和细胞核形态及结构位置进行概率解释。 通过使用我们的生成模型生成照片级真实的细胞图像，我们证明了该方法的有效性。 模型的条件特性使得在已知细胞和细胞核形态的情况下，能够预测未观察到的结构的定位。 显示英文摘要

摘要： We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.