Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
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DOI：
10.1177/2472555218818756
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发表时间：
2019-04-01
期刊：
影响因子：
3.1
通讯作者：
Spjuth, Ola
中科院分区：
文献类型：
--
作者：
Kensert, Alexander;Harrison, Philip J.;Spjuth, Ola
事实证明，从高内涵显微图像中量化和识别细胞表型对于了解不同药物治疗的生物活性非常有用。传统方法是使用经典图像分析来量化细胞形态的变化，这需要几个重要且独立的分析步骤。最近，卷积神经网络已经成为一种引人注目的替代方案，它提供了良好的预测性能，并且可以用单一网络架构取代传统工作流程。在本研究中，我们应用预训练的深度卷积神经网络 ResNet50、InceptionV3 和 InceptionResnetV2 来预测来自 Broad Bioimage Benchmark Collection 的两个细胞分析数据集响应化学扰动的细胞作用机制。这些网络在 ImageNet 上进行了预训练，从而可以更快地进行模型训练。我们获得了比之前报道的更高的预测准确度，在 95% 到 97% 之间。从少量标记数据中快速准确地区分不同细胞形态的能力说明了迁移学习和深度卷积神经网络在询问基于细胞的图像方面的综合优势。
The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several nontrivial and independent analysis steps. Recently, convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study, we applied the pretrained deep convolutional neural networks ResNet50, InceptionV3, and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pretrained on ImageNet, enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95% and 97%. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labeled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.