Scanning Electron Microscopes(SEMs)are widely used in experimental science laboratories,often requiring cumbersome and repetitive user analysis.Automating SEM image analysis processes is highly desirable to address th...Scanning Electron Microscopes(SEMs)are widely used in experimental science laboratories,often requiring cumbersome and repetitive user analysis.Automating SEM image analysis processes is highly desirable to address this challenge.In particle sample analysis,Machine Learning(ML)has emerged as the most effective approach for particle segmentation.However,the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches.Self-Supervised Learning(SSL)offers a promising alternative by enabling knowledge extraction from raw,unlabeled data.This study presents a framework for evaluating SSL techniques in SEM image analysis,focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection.A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods.The results demonstrate that ConvNeXtV2 models,with varying parameter counts,consistently outperform other techniques in particle detection across different length scales,achieving up to a34%reduction in relative error compared to established SSL methods.Furthermore,an ablation study explores the relationship between dataset size and SSL performance,providing actionable insights for practitioners regarding model selection and resource efficiency.This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.展开更多
基金funded by the German Research Foundation(DFG)under Project ID 390874152(POLiS Cluster of Excellence)N.J.S.,A.G.,G.C.,O.D.,A.J.were supported by the D2S2 program within the U.S.Department of Energy,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under Contract No.DE-AC02-05-CH11231(D2S2 program,KCD2S2)A.G.acknowledges support from the Swiss National Science Foundation(SNSF,project#P500PN_222166).
文摘Scanning Electron Microscopes(SEMs)are widely used in experimental science laboratories,often requiring cumbersome and repetitive user analysis.Automating SEM image analysis processes is highly desirable to address this challenge.In particle sample analysis,Machine Learning(ML)has emerged as the most effective approach for particle segmentation.However,the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches.Self-Supervised Learning(SSL)offers a promising alternative by enabling knowledge extraction from raw,unlabeled data.This study presents a framework for evaluating SSL techniques in SEM image analysis,focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection.A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods.The results demonstrate that ConvNeXtV2 models,with varying parameter counts,consistently outperform other techniques in particle detection across different length scales,achieving up to a34%reduction in relative error compared to established SSL methods.Furthermore,an ablation study explores the relationship between dataset size and SSL performance,providing actionable insights for practitioners regarding model selection and resource efficiency.This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.
