论文标题
深noise：基于荧光显微镜图像分类的信号和噪声解散，通过深度学习
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
论文作者
论文摘要
基于图像的高含量测定通常用于识别生物学领域遗传扰动的表型影响。但是，在实验过程中仍未解决持续的问题：由系统错误（例如，温度，试剂浓度和井位置）引起的干涉技术噪声总是与实际的生物学信号混合在一起，从而误解了任何得出的结论。在这里，我们展示了一种基于教师的平均深度学习模型（DeepNoise），该模型可以将生物学信号与实验噪声相关。具体而言，我们旨在将1,108种不同遗传扰动的表型影响分类，这些遗传扰动筛选出了125,510个荧光显微镜图像，这些荧光显微镜图像完全无法识别。我们通过参与递归细胞图像分类挑战来验证我们的模型，我们提出的方法达到了极高的分类评分（ACC：99.596％），在866个参与组中排名第二。这个有希望的结果表明生物学和技术因素成功分离，这可能有助于降低治疗成本并加快药物发现过程。
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noise caused by systematic errors (e.g., temperature, reagent concentration, and well location) is always mixed up with the real biological signals, leading to misinterpretation of any conclusion drawn. Here, we show a mean teacher based deep learning model (DeepNoise) that can disentangle biological signals from the experimental noise. Specifically, we aim to classify the phenotypic impact of 1,108 different genetic perturbations screened from 125,510 fluorescent microscopy images, which are totally unrecognizable by human eye. We validate our model by participating in the Recursion Cellular Image Classification Challenge, and our proposed method achieves an extremely high classification score (Acc: 99.596%), ranking the 2nd place among 866 participating groups. This promising result indicates the successful separation of biological and technical factors, which might help decrease the cost of treatment development and expedite the drug discovery process.
