{"id":"https://openalex.org/W7155208650","doi":"https://doi.org/10.48550/arxiv.2604.19369","title":"IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging","display_name":"IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155208650","doi":"https://doi.org/10.48550/arxiv.2604.19369"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.19369","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128905503","display_name":"Philipp Weigand","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weigand, Philipp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134221932","display_name":"Niels Nawrot","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nawrot, Niels","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051530011","display_name":"Nikolas Ebert","orcid":"https://orcid.org/0000-0002-2971-1012"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ebert, Nikolas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134218163","display_name":"Carsten Hopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hopf, Carsten","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5118746094","display_name":"Oliver Wasenm\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wasenm\u00fcller, Oliver","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/T10683","display_name":"Mass Spectrometry Techniques and Applications","score":0.919700026512146,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10683","display_name":"Mass Spectrometry Techniques and Applications","score":0.919700026512146,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":0.03440000116825104,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.00989999994635582,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular 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"}}],"keywords":[{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6801999807357788},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6032999753952026},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.597100019454956},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4805999994277954},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4771000146865845},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/ion","display_name":"Ion","score":0.38499999046325684},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3571000099182129}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6851999759674072},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6801999807357788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6256999969482422},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6032999753952026},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.597100019454956},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4496999979019165},{"id":"https://openalex.org/C145148216","wikidata":"https://www.wikidata.org/wiki/Q36496","display_name":"Ion","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C162356407","wikidata":"https://www.wikidata.org/wiki/Q180809","display_name":"Mass spectrometry","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3476000130176544},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2831999957561493},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.25839999318122864},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C24066741","wikidata":"https://www.wikidata.org/wiki/Q3796564","display_name":"Mass spectrometry imaging","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2604.19369","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19369","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":{"Peak":[0],"picking":[1,54,97,111],"is":[2,15],"a":[3,42,103,135],"fundamental":[4],"preprocessing":[5],"step":[6],"in":[7,75,142],"Mass":[8],"Spectrometry":[9],"Imaging":[10],"(MSI),":[11],"where":[12],"each":[13],"sample":[14],"represented":[16],"by":[17,113,152],"hundreds":[18],"to":[19,34,78,118,154],"thousands":[20],"of":[21],"ion":[22,47,76,92,166],"images.":[23],"Existing":[24],"approaches":[25],"require":[26],"careful":[27],"dataset-specific":[28],"hyperparameter":[29,100],"tuning,":[30],"and":[31,66,94,172],"often":[32],"fail":[33],"generalize":[35],"across":[36,121],"acquisition":[37],"protocols.":[38],"We":[39,59],"introduce":[40],"IonMorphNet,":[41],"spatial-structure-aware":[43],"representation":[44],"model":[45,173],"for":[46,83,138],"images":[48,77,93],"that":[49,129],"enables":[50,134],"fully":[51],"data-driven":[52],"peak":[53,96,110,125],"without":[55,98],"any":[56],"task-specific":[57],"supervision.":[58],"curate":[60],"53":[61],"publicly":[62],"available":[63,176],"MSI":[64],"datasets":[65],"define":[67],"six":[68],"structural":[69,84],"classes":[70],"capturing":[71],"representative":[72],"spatial":[73],"patterns":[74],"train":[79],"standard":[80],"image":[81,167],"backbones":[82],"pattern":[85],"classification.":[86],"Once":[87],"trained,":[88],"IonMorphNet":[89],"can":[90],"assess":[91],"perform":[95],"additional":[99],"tuning.":[101],"Using":[102],"ConvNeXt":[104],"V2-Tiny":[105],"backbone,":[106],"our":[107],"approach":[108,145],"improves":[109],"performance":[112],"+7":[114],"%":[115,156],"mSCF1":[116],"compared":[117],"state-of-the-art":[119],"methods":[120],"multiple":[122],"datasets.":[123],"Beyond":[124],"picking,":[126],"we":[127],"demonstrate":[128],"spatially":[130],"informed":[131],"channel":[132],"reduction":[133],"3D":[136],"CNN":[137],"patch-based":[139],"tumor":[140,161],"classification":[141,162],"MSI.":[143],"This":[144],"matches":[146],"or":[147],"exceeds":[148],"pixel-wise":[149],"spectral":[150],"classifiers":[151],"up":[153],"+7.3":[155],"Balanced":[157],"Accuracy":[158],"on":[159],"three":[160],"tasks,":[163],"indicating":[164],"meaningful":[165],"selection.":[168],"The":[169],"source":[170],"code":[171],"weights":[174],"are":[175],"at":[177],"https://github.com/CeMOS-IS/IonMorphNet.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-23T00:00:00"}