DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning  

作　　者：Sen Yang Tao Shen Yuqi Fang Xiyue Wang Jun Zhang Wei Yang Junzhou Huang Xiao Han 

机构地区：[1]Tencent AI Lab,Shenzhen 518057,China [2]Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong Special Administrative Region 999077,China [3]College of Computer Science,Sichuan University,Chengdu 610065,China

出　　处：《Genomics, Proteomics & Bioinformatics》2022年第5期989-1001,共13页基因组蛋白质组与生物信息学报（英文版）

摘　　要：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 noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy: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.The source code of Deep Noise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

关 键 词：Fluorescent microscopy image Biological signal Classification Deep learning Genetic perturbation 

分 类 号：Q-334[生物学] TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]