{"id":"https://openalex.org/W7163211189","doi":"https://doi.org/10.48550/arxiv.2606.02532","title":"Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation","display_name":"Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163211189","doi":"https://doi.org/10.48550/arxiv.2606.02532"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02532","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.02532","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137703726","display_name":"Ni Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044819021","display_name":"Nuohao Liu","orcid":"https://orcid.org/0000-0003-3526-0980"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Nuohao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137648978","display_name":"Ryan Jacobs","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jacobs, Ryan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061068638","display_name":"Ajay Annamareddy","orcid":"https://orcid.org/0000-0002-8081-165X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Annamareddy, Ajay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137639252","display_name":"Maciej P. Polak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Polak, Maciej P.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137671759","display_name":"Kevin Field","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Field, Kevin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020464630","display_name":"Izabela Szlufarska","orcid":"https://orcid.org/0000-0002-5895-8620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Szlufarska, Izabela","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137711127","display_name":"Dane Morgan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morgan, Dane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.6510000228881836,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.6510000228881836,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.06319999694824219,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05979999899864197,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6572999954223633},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5654000043869019},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5077999830245972},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4302000105381012},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4074000120162964},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.399399995803833},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3456999957561493}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6855000257492065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5654000043869019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.564300000667572},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5077999830245972},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3456999957561493},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3073999881744385},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.2973000109195709},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2779280203","wikidata":"https://www.wikidata.org/wiki/Q17121211","display_name":"Small data","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02532","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.02532","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02532","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Analyzing":[0],"microstructural":[1],"defects":[2],"in":[3,10,128,172],"transmission":[4],"electron":[5],"microscopy":[6],"(TEM)":[7],"images,":[8],"particularly":[9],"irradiated":[11],"metal":[12],"alloys,":[13],"is":[14],"often":[15],"limited":[16],"by":[17,68],"the":[18,62,129,143,153,161],"availability":[19],"of":[20,64,85,132,163],"high-quality,":[21],"labeled":[22,49,92],"data.":[23],"To":[24],"address":[25],"this,":[26],"we":[27,139],"introduce":[28],"a":[29,35,97,125],"generative":[30,114,165],"data":[31,77,156],"augmentation":[32,115],"approach":[33],"using":[34],"mask-conditioned":[36],"latent":[37],"diffusion":[38],"model":[39,104,119],"(LDM)":[40],"for":[41,57,105],"synthesizing":[42],"realistic":[43],"TEM":[44],"images":[45],"with":[46,122],"controllable,":[47],"automatically":[48],"multi-class":[50],"defect":[51,106],"masks.":[52,74],"Without":[53],"requiring":[54],"manual":[55],"annotations":[56],"generation,":[58],"our":[59],"method":[60],"enables":[61],"creation":[63],"synthetic":[65],"image-mask":[66],"pairs":[67],"sampling":[69],"distributions":[70],"learned":[71],"from":[72],"experimental":[73,83,93],"These":[75,158],"generated":[76],"were":[78],"used":[79],"to":[80,95,124,146,167],"augment":[81],"small":[82,117],"datasets":[84],"varying":[86],"sizes":[87],"(10,":[88],"50,":[89],"and":[90,108,134,148],"100":[91],"images)":[94],"train":[96],"Mask":[98],"Regional":[99],"Convolutional":[100],"Neural":[101],"Network":[102],"(R-CNN)":[103],"detection":[107,133,147],"classification.":[109],"Our":[110],"results":[111],"show":[112],"that":[113,142],"yields":[116],"overall":[118],"performance":[120,171],"improvements,":[121],"up":[123],"0.02":[126],"gain":[127],"harmonic":[130],"mean":[131],"classification":[135,149],"F1":[136],"scores.":[137],"However,":[138],"also":[140],"find":[141],"relative":[144],"contributions":[145],"improvement":[150],"depend":[151],"on":[152],"specific":[154],"train/test":[155],"split.":[157],"findings":[159],"highlight":[160],"potential":[162],"targeted":[164],"models":[166],"enhance":[168],"deep":[169],"learning":[170],"data-scarce":[173],"microscopy-based":[174],"image":[175],"quantification":[176],"tasks.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}