{"id":"https://openalex.org/W4416250680","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227885","title":"Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks","display_name":"Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250680","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227885"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.22399","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036717593","display_name":"Luca Bergamin","orcid":"https://orcid.org/0000-0002-0662-7862"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Luca Bergamin","raw_affiliation_strings":["University of Padua,Department of Mathematics,Padova,Italy"],"affiliations":[{"raw_affiliation_string":"University of Padua,Department of Mathematics,Padova,Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018615713","display_name":"Giovanna Maria Dimitri","orcid":"https://orcid.org/0000-0002-2728-4272"},"institutions":[{"id":"https://openalex.org/I102064193","display_name":"University of Siena","ror":"https://ror.org/01tevnk56","country_code":"IT","type":"education","lineage":["https://openalex.org/I102064193"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giovanna Maria Dimitri","raw_affiliation_strings":["University of Siena,Department of Information Engineering and Mathematics,Siena,Italy"],"affiliations":[{"raw_affiliation_string":"University of Siena,Department of Information Engineering and Mathematics,Siena,Italy","institution_ids":["https://openalex.org/I102064193"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036243123","display_name":"Fabio Aiolli","orcid":"https://orcid.org/0000-0002-5823-7540"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabio Aiolli","raw_affiliation_strings":["University of Padua,Department of Mathematics,Padova,Italy"],"affiliations":[{"raw_affiliation_string":"University of Padua,Department of Mathematics,Padova,Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036717593"],"corresponding_institution_ids":["https://openalex.org/I138689650"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17949916,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.12470000237226486,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.12470000237226486,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.09870000183582306,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.08320000022649765,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7527999877929688},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6294999718666077},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4846000075340271},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.46320000290870667},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41280001401901245},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4043999910354614},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.3926999866962433},{"id":"https://openalex.org/keywords/medical-knowledge","display_name":"Medical knowledge","score":0.3781999945640564},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3677000105381012}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7527999877929688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6837999820709229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6765999794006348},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6294999718666077},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4846000075340271},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42829999327659607},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41280001401901245},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3926999866962433},{"id":"https://openalex.org/C2985722590","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medical knowledge","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36090001463890076},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3336000144481659},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3158000111579895},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C102993220","wikidata":"https://www.wikidata.org/wiki/Q387196","display_name":"Description logic","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.22399","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.22399","pdf_url":"https://arxiv.org/pdf/2509.22399","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:www.research.unipd.it:11577/3569500","is_oa":false,"landing_page_url":"https://hdl.handle.net/11577/3569500","pdf_url":null,"source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.22399","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.22399","pdf_url":"https://arxiv.org/pdf/2509.22399","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2135881201","https://openalex.org/W2412782625","https://openalex.org/W2942904318","https://openalex.org/W2995495466","https://openalex.org/W3035665735","https://openalex.org/W3113149630","https://openalex.org/W3138516171","https://openalex.org/W3172681723","https://openalex.org/W3198250154","https://openalex.org/W3216178672","https://openalex.org/W4221064623","https://openalex.org/W4221155892","https://openalex.org/W4221163766","https://openalex.org/W4286568264","https://openalex.org/W4377041101","https://openalex.org/W4382364614","https://openalex.org/W4387617742","https://openalex.org/W4406893186","https://openalex.org/W4408387649"],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,69,147,178],"is":[2,153],"a":[3,119],"fundamental":[4],"task":[5,130],"in":[6,18,22,41,114,135,157],"medical":[7,11,65,84,176],"image":[8],"analysis,":[9],"aiding":[10],"decision-making":[12],"by":[13,28,62],"helping":[14],"radiologists":[15],"distinguish":[16],"objects":[17],"an":[19,115],"image.":[20],"Research":[21],"this":[23,74],"field":[24],"has":[25],"been":[26],"driven":[27],"deep":[29],"learning":[30],"applications,":[31],"which":[32],"have":[33],"the":[34,42,68,99,102,129,133,145],"potential":[35],"to":[36,82,105,169,174],"scale":[37],"these":[38,49],"systems":[39,50],"even":[40],"presence":[43],"of":[44,101,131],"noise":[45],"and":[46,172],"artifacts.":[47],"However,":[48],"are":[51,166],"not":[52],"yet":[53],"perfected.":[54],"We":[55,111,124],"argue":[56,162],"that":[57,142,163],"performance":[58],"can":[59],"be":[60,170],"improved":[61],"incorporating":[63],"common":[64],"knowledge":[66,86],"into":[67],"model\u2019s":[70],"loss":[71],"function.":[72],"To":[73],"end,":[75],"we":[76,161],"introduce":[77],"Logic":[78],"Tensor":[79],"Networks":[80],"(LTNs)":[81],"encode":[83],"background":[85],"using":[87],"first-order":[88],"logic":[89],"(FOL)":[90],"rules.":[91],"The":[92],"encoded":[93],"rules":[94],"span":[95],"from":[96],"constraints":[97],"on":[98,128],"shape":[100],"produced":[103],"segmentation,":[104],"relationships":[106],"between":[107],"different":[108],"segmented":[109],"areas.":[110],"apply":[112],"LTNs":[113,143],"end-to-end":[116],"framework":[117],"with":[118],"SwinUNETR":[120],"for":[121],"semantic":[122,177],"segmentation.":[123],"evaluate":[125],"our":[126],"method":[127],"segmenting":[132],"hippocampus":[134],"brain":[136],"MRI":[137],"scans.":[138],"Our":[139],"experiments":[140],"show":[141],"improve":[144],"baseline":[146],"performance,":[148],"especially":[149],"when":[150],"training":[151],"data":[152],"scarce.":[154],"Despite":[155],"being":[156],"its":[158],"preliminary":[159],"stages,":[160],"neurosymbolic":[164],"methods":[165],"general":[167],"enough":[168],"adapted":[171],"applied":[173],"other":[175],"tasks.":[179]},"counts_by_year":[],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
