Computational modeling of cellular structures using conditional deep generative networks
Computational modeling of cellular structures using conditional deep generative networks
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DOI：
10.1093/bioinformatics/bty923
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发表时间：
2019-06-15
期刊：
影响因子：
5.8
通讯作者：
Ji, Shuiwang
中科院分区：
文献类型：
--
作者：
Yuan, Hao;Cai, Lei;Ji, Shuiwang
动机细胞功能与其亚结构的定位密切相关。然而，在同一细胞中同时实验标记所有亚细胞结构是具有挑战性的。这就需要建立一个计算模型来学习这些亚细胞结构之间的关系，并使用参考结构来推断其他结构的定位。结果我们将这样的任务制定为条件图像生成问题，并建议使用条件生成对抗网络来解决它。解码器网络作为生成器，并提出使用编码器和解码器之间的跳过连接来向解码器提供空间信息。为了以各种不同的方式整合条件信息，我们开发了三种不同类型的跳过连接，称为自选通连接，编码器选通连接和标签选通连接。所提出的跳过连接是基于条件信息使用门控机制建立的。通过学习门控函数，网络能够控制哪些信息应该通过跳过连接从编码器传递到解码器。由于门参数也是自动学习的，我们希望只有有用的空间信息被传输到解码器，以帮助图像生成。我们进行定性和定量评估，以评估我们提出的方法的有效性。实验结果表明，我们基于cGAN的方法能够正确生成所需的亚细胞结构。我们的研究结果还表明，所提出的方法优于现有的基于对抗性自动编码器的方法，新的跳过连接导致性能的提高。此外，通过我们的方法生成的亚细胞结构的定位与生物实验中的观察结果一致。可用性和实现源代码和更多结果可在https://github.com/divelab/cgan/上获得。
Motivation Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures.Results We formulate such a task as a conditional image generation problem and propose to use conditional generative adversarial networks for tackling it. We employ an encoder-decoder network as the generator and propose to use skip connections between the encoder and decoder to provide spatial information to the decoder. To incorporate the conditional information in a variety of different ways, we develop three different types of skip connections, known as the self-gated connection, encoder-gated connection and label-gated connection. The proposed skip connections are built based on the conditional information using gating mechanisms. By learning a gating function, the network is able to control what information should be passed through the skip connections from the encoder to the decoder. Since the gate parameters are also learned automatically, we expect that only useful spatial information is transmitted to the decoder to help image generation. We perform both qualitative and quantitative evaluations to assess the effectiveness of our proposed approaches. Experimental results show that our cGAN-based approaches have the ability to generate the desired sub-cellular structures correctly. Our results also demonstrate that the proposed approaches outperform the existing approach based on adversarial auto-encoders, and the new skip connections lead to improved performance. In addition, the localizations of generated sub-cellular structures by our approaches are consistent with observations in biological experiments.Availability and implementation The source code and more results are available at https://github.com/divelab/cgan/.