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.

本文提出两种新方法对影像数据进行时间和形态表型效应研究并将其验证于 2D 癌细胞培养物中以分析药物效果。通过使用 1 百万张带有多种增强技术和多个裁剪的图像数据进行卷积自编码器的训练，将其作为特征提取器并通过距离分析和 DTW 动态时间规整应用于 31 种药物的时间模式聚类。使用对抗 / 正则化设置改善 31 种药物的分类并可视化图像区域，提高了平均 8％的排名和发现了一些形态特征的重要性图。作者在文中提供了这一特征提取器和权重以促进生物学中的迁移学习应用，并讨论了其他预训练模型的实用性以及该方法对其他类型的生物医学数据的适用性。