论文标题
在癌细胞成像数据中分析时间和形态药物效应的自我监督学习
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data
论文作者
论文摘要
在这项工作中，我们提出了两种新的方法，用于研究使用成像数据不同的实验条件引起的时间和形态表型效应。作为概念证明，我们将它们应用于2D癌细胞培养物中的药物作用。我们在1M图像数据集上训练卷积自动编码器，并具有随机的增强和多工件，以用作特征提取器。我们将其系统地比较它与预处理的最新模型。我们进一步以两种方式使用功能提取器。首先，我们将基于距离的分析和动态时间扭曲应用于31种药物的群集时间模式。我们确定允许对药物注释的簇是具有细胞毒性，细胞抑制，混合或无作用的簇。其次，我们实施了对抗性/正规学习设置，以改善31种药物的分类并想象有助于改进的图像区域。我们平均将前3个分类精度提高了8％，并且是形态特征重要的图。我们提供特征提取器和权重，以促进生物学中的转移学习应用。我们还讨论了其他审慎模型的实用性以及我们方法对其他类型的生物医学数据的适用性。
In this work, we propose two novel methodologies to study temporal and morphological phenotypic effects caused by different experimental conditions using imaging data. As a proof of concept, we apply them to analyze drug effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M images dataset with random augmentations and multi-crops to use as feature extractor. We systematically compare it to the pretrained state-of-the-art models. We further use the feature extractor in two ways. First, we apply distance-based analysis and dynamic time warping to cluster temporal patterns of 31 drugs. We identify clusters allowing annotation of drugs as having cytotoxic, cytostatic, mixed or no effect. Second, we implement an adversarial/regularized learning setup to improve classification of 31 drugs and visualize image regions that contribute to the improvement. We increase top-3 classification accuracy by 8% on average and mine examples of morphological feature importance maps. We provide the feature extractor and the weights to foster transfer learning applications in biology. We also discuss utility of other pretrained models and applicability of our methods to other types of biomedical data.
