{"id":"https://openalex.org/W4400491453","doi":"https://doi.org/10.1109/aiiot61789.2024.10579036","title":"Enhanced Segmentation in Abdominal CT Images: Leveraging Hybrid CNN-Transformer Architectures and Compound Loss Function","display_name":"Enhanced Segmentation in Abdominal CT Images: Leveraging Hybrid CNN-Transformer Architectures and Compound Loss Function","publication_year":2024,"publication_date":"2024-05-29","ids":{"openalex":"https://openalex.org/W4400491453","doi":"https://doi.org/10.1109/aiiot61789.2024.10579036"},"language":"en","primary_location":{"id":"doi:10.1109/aiiot61789.2024.10579036","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aiiot61789.2024.10579036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE World AI IoT Congress (AIIoT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078156687","display_name":"Fatemeh Piri","orcid":null},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Fatemeh Piri","raw_affiliation_strings":["Isfahan University of Technology,Department of Electrical and Computer Engineering,Isfahan,Iran,84156-83111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Isfahan University of Technology,Department of Electrical and Computer Engineering,Isfahan,Iran,84156-83111","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086843767","display_name":"Nader Karimi","orcid":"https://orcid.org/0000-0001-8904-1607"},"institutions":[{"id":"https://openalex.org/I170013655","display_name":"Isfahan University of Technology","ror":"https://ror.org/00af3sa43","country_code":"IR","type":"education","lineage":["https://openalex.org/I170013655"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Nader Karimi","raw_affiliation_strings":["Isfahan University of Technology,Department of Electrical and Computer Engineering,Isfahan,Iran,84156-83111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Isfahan University of Technology,Department of Electrical and Computer Engineering,Isfahan,Iran,84156-83111","institution_ids":["https://openalex.org/I170013655"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001420311","display_name":"Shadrokh Samavi","orcid":"https://orcid.org/0000-0003-3951-3770"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shadrokh Samavi","raw_affiliation_strings":["Seattle University,Department of Computer Science,Seattle,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seattle University,Department of Computer Science,Seattle,USA","institution_ids":["https://openalex.org/I58610484"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3658,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62312383,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"34","issue":null,"first_page":"363","last_page":"369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9926000237464905,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9926000237464905,"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"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9894000291824341,"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/computer-science","display_name":"Computer science","score":0.6158735156059265},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5903611183166504},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5739407539367676},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5278313755989075},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5096888542175293},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5067048668861389},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3924518823623657},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.12379249930381775},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12356004118919373},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.0704193115234375}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6158735156059265},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5903611183166504},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5739407539367676},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5278313755989075},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5096888542175293},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5067048668861389},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3924518823623657},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.12379249930381775},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12356004118919373},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0704193115234375}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aiiot61789.2024.10579036","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aiiot61789.2024.10579036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE World AI IoT Congress (AIIoT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2787091153","https://openalex.org/W2804860796","https://openalex.org/W2888358068","https://openalex.org/W2907750714","https://openalex.org/W2962914239","https://openalex.org/W2964227007","https://openalex.org/W3094502228","https://openalex.org/W3097510987","https://openalex.org/W3127751679","https://openalex.org/W3138516171","https://openalex.org/W3171206729","https://openalex.org/W3203841574","https://openalex.org/W4214588794","https://openalex.org/W4319300975","https://openalex.org/W4321232185","https://openalex.org/W4390345094","https://openalex.org/W4394625805","https://openalex.org/W6790275670","https://openalex.org/W6803916128"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4395685956","https://openalex.org/W2799953226","https://openalex.org/W4398146871","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,30],"of":[2,42,85,107,119],"abdominal":[3],"organs":[4],"in":[5,19],"CT":[6],"scans":[7],"is":[8],"essential":[9],"for":[10,28,60,73,114],"medical":[11,120],"diagnosis":[12],"and":[13,47,89],"treatment.":[14],"This":[15,49,79],"paper":[16],"addresses":[17,82],"limitations":[18],"current":[20],"methods":[21],"by":[22],"proposing":[23],"an":[24],"enhanced":[25],"HiFormer":[26],"model":[27,50],"improved":[29],"accuracy.":[31],"We":[32],"introduce":[33],"a":[34,66,104],"novel":[35],"hybrid":[36],"architecture":[37],"that":[38],"combines":[39],"the":[40,57,83,96,116],"strengths":[41],"convolutional":[43],"neural":[44],"networks":[45],"(CNNs)":[46],"transformers.":[48],"incorporates":[51],"Cross-covariance":[52],"image":[53,121],"Transformer":[54],"blocks":[55],"within":[56],"encoder,":[58],"allowing":[59],"efficient":[61],"spatial":[62],"information":[63],"processing.":[64],"Additionally,":[65],"compound":[67],"DiceTopK":[68],"loss":[69],"function":[70],"optimizes":[71],"training":[72],"better":[74],"handling":[75],"organ":[76,86],"size":[77,87],"variations.":[78],"approach":[80],"effectively":[81],"challenges":[84],"variability":[88],"robustness,":[90],"surpassing":[91],"baseline":[92],"models.":[93],"Evaluations":[94],"on":[95],"Synapse":[97],"multi-organ":[98],"dataset":[99],"demonstrate":[100],"significant":[101],"improvements,":[102],"achieving":[103],"Dice":[105],"score":[106],"81.15.":[108],"The":[109],"proposed":[110],"method":[111],"holds":[112],"promise":[113],"enhancing":[115],"clinical":[117],"applications":[118],"analysis.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}