DeepNoise:Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning
- Author:
Yang SEN
1
;
Shen TAO
;
Fang YUQI
;
Wang XIYUE
;
Zhang JUN
;
Yang WEI
;
Huang JUNZHOU
;
Han XIAO
Author Information
1. Tencent AI Lab,Shenzhen 518057,China
- Keywords:
Fluorescent microscopy image;
Biological signal;
Classification;
Deep learning;
Genetic perturbation
- From:
Genomics, Proteomics & Bioinformatics
2022;20(5):989-1001
- CountryChina
- Language:Chinese
-
Abstract:
The high-content image-based assay is commonly leveraged for identifying the pheno-typic 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.,tempera-ture,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(DeepNoise)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 DeepNoise achieved an extremely high classification score(accuracy:99.596％),ranking the 2nd place among 866 participating groups.This promising result indicates the success-ful 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 DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.
