{"id":"https://openalex.org/W4408345658","doi":"https://doi.org/10.1109/icassp49660.2025.10889067","title":"CabiNet: A Deep Learning Framework for Multiclass Medical Image Segmentation from Multiple Single Class Datasets","display_name":"CabiNet: A Deep Learning Framework for Multiclass Medical Image Segmentation from Multiple Single Class Datasets","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345658","doi":"https://doi.org/10.1109/icassp49660.2025.10889067"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5015098367","display_name":"Aman Soni","orcid":null},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aman Soni","raw_affiliation_strings":["IIT Kharagpur,Electrical Engineering,Kharagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Kharagpur,Electrical Engineering,Kharagpur,India","institution_ids":["https://openalex.org/I145894827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041419939","display_name":"Ishita Maiti","orcid":null},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ishita Maiti","raw_affiliation_strings":["IIT Kharagpur,School of Medical Science and Technology,Kharagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Kharagpur,School of Medical Science and Technology,Kharagpur,India","institution_ids":["https://openalex.org/I145894827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047391966","display_name":"Nirmalya Ghosh","orcid":"https://orcid.org/0000-0002-3967-2117"},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nirmalya Ghosh","raw_affiliation_strings":["IIT Kharagpur,Electrical Engineering,Kharagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Kharagpur,Electrical Engineering,Kharagpur,India","institution_ids":["https://openalex.org/I145894827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145894827"],"apc_list":null,"apc_paid":null,"fwci":2.3704,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84919514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9588000178337097,"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":0.9588000178337097,"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/T10862","display_name":"AI in cancer detection","score":0.9508000016212463,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9182000160217285,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7249740958213806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7071862816810608},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.615886390209198},{"id":"https://openalex.org/keywords/cabinet","display_name":"Cabinet (room)","score":0.5881373882293701},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5826118588447571},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.562221348285675},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46275827288627625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43643367290496826},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4238562285900116},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33850815892219543},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05993002653121948}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7249740958213806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7071862816810608},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.615886390209198},{"id":"https://openalex.org/C54745167","wikidata":"https://www.wikidata.org/wiki/Q1515062","display_name":"Cabinet (room)","level":2,"score":0.5881373882293701},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5826118588447571},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.562221348285675},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46275827288627625},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43643367290496826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4238562285900116},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33850815892219543},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05993002653121948},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889067","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2066517105","https://openalex.org/W2101637614","https://openalex.org/W2115733720","https://openalex.org/W2153431772","https://openalex.org/W2167896953","https://openalex.org/W2194775991","https://openalex.org/W2592929672","https://openalex.org/W2750806355","https://openalex.org/W2798683932","https://openalex.org/W2798753173","https://openalex.org/W2888493720","https://openalex.org/W2892308833","https://openalex.org/W2952234052","https://openalex.org/W2962825119","https://openalex.org/W2963078159","https://openalex.org/W2964950992","https://openalex.org/W2998508940","https://openalex.org/W3034199407","https://openalex.org/W3034942609","https://openalex.org/W3035665735","https://openalex.org/W3092540957","https://openalex.org/W3097337894","https://openalex.org/W3135022261","https://openalex.org/W3176031707","https://openalex.org/W3197957534","https://openalex.org/W4213225351","https://openalex.org/W4224227045","https://openalex.org/W4287448984","https://openalex.org/W4312212137","https://openalex.org/W4319216049","https://openalex.org/W4319300502","https://openalex.org/W4321232185","https://openalex.org/W4385805017","https://openalex.org/W4390562731","https://openalex.org/W6637373629","https://openalex.org/W6743428213","https://openalex.org/W6766621745","https://openalex.org/W6772669916","https://openalex.org/W6790275670","https://openalex.org/W6902995573"],"related_works":["https://openalex.org/W2501469987","https://openalex.org/W4255516540","https://openalex.org/W2466009385","https://openalex.org/W4239115058","https://openalex.org/W4255186370","https://openalex.org/W3021916084","https://openalex.org/W4249077291","https://openalex.org/W2473974764","https://openalex.org/W1600996553","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,29],"development":[1],"of":[2,15,57,59,114,125],"deep":[3],"learning":[4,91],"based":[5],"image":[6,34],"segmentation":[7,35,56,63,95],"algorithms":[8],"is":[9],"often":[10,40],"hindered":[11],"by":[12,90],"the":[13,37,47,134],"lack":[14],"adequately":[16],"annotated":[17,98],"datasets,":[18],"and":[19,55,74,111,171],"this":[20],"issue":[21],"becomes":[22,49],"a":[23,43,66,81,112],"severe":[24],"bottleneck":[25,89],"in":[26,69],"multi-class":[27,53,92],"segmentation.":[28],"models":[30],"can":[31],"learn":[32],"binary":[33],"since":[36],"datasets":[38],"are":[39,118,130],"labeled":[41],"for":[42,52,142],"single":[44],"class.":[45],"However,":[46],"task":[48],"more":[50],"challenging":[51],"annotations":[54],"regions":[58],"interest,":[60],"e.g.,":[61],"multi-organ":[62],"that":[64,84],"plays":[65],"vital":[67],"role":[68],"computer-assisted":[70],"diagnosis,":[71],"surgery":[72],"planning,":[73],"other":[75],"related":[76],"applications.":[77],"This":[78],"study":[79],"proposes":[80],"framework,":[82],"CabiNet,":[83],"attempts":[85],"to":[86,120,132],"solve":[87],"data":[88],"healthy":[93],"organ":[94,107],"from":[96],"partially":[97],"abdominal":[99],"MRI":[100,153],"data,":[101],"i.e.,":[102],"multiple":[103],"single-class":[104],"datasets.":[105],"Multiple":[106],"specific":[108],"expert":[109],"networks":[110],"Jack":[113],"All":[115],"(JoA)":[116],"network":[117],"trained":[119],"generate":[121],"posterior":[122,136],"probability":[123,137],"maps":[124],"individual":[126],"organs":[127,141],"simultaneously":[128],"which":[129],"accumulated":[131],"get":[133],"final":[135],"distributions":[138],"over":[139],"different":[140],"each":[143],"pixel.":[144],"On":[145],"benchmark":[146],"Combined":[147],"Healthy":[148],"Abdominal":[149],"Organ":[150],"Segmentation":[151],"(CHAOS)":[152],"dataset":[154],"CabiNet":[155],"yielded":[156],"very":[157],"promising":[158],"dice":[159,174],"scores:":[160],"liver":[161],"(90.39%),":[162],"right":[163],"kidney":[164,167],"(87.41%),":[165],"left":[166],"(81.09%),":[168],"spleen":[169],"(90.78%)":[170],"overall":[172],"average":[173],"score":[175],"as":[176],"87.4%.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
