{"id":"https://openalex.org/W4402954567","doi":"https://doi.org/10.48550/arxiv.2409.01330","title":"Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort","display_name":"Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4402954567","doi":"https://doi.org/10.48550/arxiv.2409.01330"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2409.01330","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.01330","pdf_url":"https://arxiv.org/pdf/2409.01330","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.01330","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052429045","display_name":"Iulian Emil Tampu","orcid":"https://orcid.org/0000-0002-7582-1706"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tampu, Iulian Emil","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054143174","display_name":"Per Olof Nyman","orcid":"https://orcid.org/0000-0001-8921-431X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nyman, Per","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107589369","display_name":"Christoforos Spyretos","orcid":"https://orcid.org/0009-0001-8127-0867"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spyretos, Christoforos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076640188","display_name":"Ida Blystad","orcid":"https://orcid.org/0000-0002-8857-5698"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Blystad, Ida","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086139984","display_name":"Alia Shamikh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shamikh, Alia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089447800","display_name":"Gabriela Prochazka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prochazka, Gabriela","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022568973","display_name":"Teresita D\u00edaz de St\u00e5hl","orcid":"https://orcid.org/0000-0001-5933-6623"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de St\u00e5hl, Teresita D\u00edaz","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041031533","display_name":"Johanna Sandgren","orcid":"https://orcid.org/0000-0001-6776-2649"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandgren, Johanna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035976768","display_name":"Peter Lundberg","orcid":"https://orcid.org/0000-0001-8661-2232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lundberg, Peter","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083149360","display_name":"Neda Haj\u2010Hosseini","orcid":"https://orcid.org/0000-0002-0555-8877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haj-Hosseini, Neda","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5052429045"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T10862","display_name":"AI in cancer detection","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9732999801635742,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9688000082969666,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/histopathology","display_name":"Histopathology","score":0.7267340421676636},{"id":"https://openalex.org/keywords/center","display_name":"Center (category theory)","score":0.6313667893409729},{"id":"https://openalex.org/keywords/cohort","display_name":"Cohort","score":0.6039600968360901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.532871425151825},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46695953607559204},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.4349668025970459},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.37147435545921326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32897305488586426},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.29288434982299805}],"concepts":[{"id":"https://openalex.org/C544855455","wikidata":"https://www.wikidata.org/wiki/Q1070952","display_name":"Histopathology","level":2,"score":0.7267340421676636},{"id":"https://openalex.org/C2779463800","wikidata":"https://www.wikidata.org/wiki/Q5062222","display_name":"Center (category theory)","level":2,"score":0.6313667893409729},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.6039600968360901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.532871425151825},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46695953607559204},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.4349668025970459},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.37147435545921326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32897305488586426},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.29288434982299805},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2409.01330","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.01330","pdf_url":"https://arxiv.org/pdf/2409.01330","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2409.01330","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.01330","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.01330","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.01330","pdf_url":"https://arxiv.org/pdf/2409.01330","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3039419443","https://openalex.org/W4386772532","https://openalex.org/W4393212117","https://openalex.org/W2115661411","https://openalex.org/W2399391471","https://openalex.org/W2400254106","https://openalex.org/W2970729894","https://openalex.org/W4381996710","https://openalex.org/W4294739205","https://openalex.org/W4308553438"],"abstract_inverted_index":{"Brain":[0],"tumors":[1,7,52,237],"are":[2],"the":[3,14,22,80,125,148,208,213,226],"most":[4],"common":[5],"solid":[6],"in":[8,27,53,210,233],"children":[9],"and":[10,55,99,135,150,174,184,190,200],"young":[11],"adults,":[12],"but":[13],"scarcity":[15],"of":[16,24,128,147,181,228],"large":[17],"histopathology":[18],"datasets":[19],"has":[20],"limited":[21],"application":[23],"computational":[25,230],"pathology":[26,231],"this":[28],"group.":[29],"This":[30],"study":[31],"implements":[32],"two":[33,146],"weakly":[34],"supervised":[35],"multiple-instance":[36],"learning":[37],"(MIL)":[38],"approaches":[39],"on":[40,119,124,143,152,245],"patch-features":[41],"obtained":[42,89],"from":[43,61,67,79,90,145,154,212],"state-of-the-art":[44,229],"histology-specific":[45],"foundation":[46],"models":[47,197],"to":[48,215],"classify":[49],"pediatric":[50,129,235],"brain":[51,75,130,236],"hematoxylin":[54],"eosin":[56],"whole":[57],"slide":[58],"images":[59],"(WSIs)":[60],"a":[62,246],"multi-center":[63,247],"Swedish":[64,82],"cohort.":[65],"WSIs":[66,91],"540":[68],"subjects":[69],"(age":[70],"8.5$\\pm$4.9":[71],"years)":[72],"diagnosed":[73],"with":[74,177,242],"tumor":[76,132,187],"were":[77,88,102,117],"gathered":[78],"six":[81],"university":[83],"hospitals.":[84],"Instance":[85],"(patch)-level":[86],"features":[87,173,202],"using":[92,104,171,205],"three":[93,120],"pre-trained":[94],"feature":[95,221],"extractors:":[96],"ResNet50,":[97],"UNI,":[98],"CONCH.":[100],"Instances":[101],"aggregated":[103],"attention-based":[105],"MIL":[106,111],"(ABMIL)":[107],"or":[108],"clustering-constrained":[109],"attention":[110,163],"(CLAM)":[112],"for":[113,186],"patient-level":[114],"classification.":[115],"Models":[116],"evaluated":[118,161],"classification":[121,127,167],"tasks":[122],"based":[123],"hierarchical":[126,240],"tumors:":[131],"category,":[133,188],"family,":[134,189],"type.":[136],"Model":[137,158],"generalization":[138],"was":[139,160,169,218],"assessed":[140],"by":[141],"training":[142],"data":[144,153],"centers":[149],"testing":[151,217],"four":[155],"other":[156],"centers.":[157],"interpretability":[159],"through":[162],"mapping.":[164],"The":[165],"highest":[166],"performance":[168,211],"achieved":[170],"UNI":[172,199],"ABMIL":[175],"aggregation,":[176],"Matthew's":[178],"correlation":[179],"coefficient":[180],"0.76$\\pm$0.04,":[182],"0.63$\\pm$0.04,":[183],"0.60$\\pm$0.05":[185],"type":[191],"classification,":[192],"respectively.":[193],"When":[194],"evaluating":[195],"generalization,":[196],"utilizing":[198],"CONCH":[201],"outperformed":[203],"those":[204],"ResNet50.":[206],"However,":[207],"drop":[209],"in-site":[214],"out-of-site":[216],"similar":[219],"across":[220],"extractors.":[222],"These":[223],"results":[224],"show":[225],"potential":[227],"methods":[232],"diagnosing":[234],"at":[238],"different":[239],"levels":[241],"fair":[243],"generalizability":[244],"national":[248],"dataset.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}