{"id":"https://openalex.org/W4388937220","doi":"https://doi.org/10.1109/icccnt56998.2023.10307210","title":"Nuclei Segmentation using EfficientNetV2 and Convolutional Block Attention Module","display_name":"Nuclei Segmentation using EfficientNetV2 and Convolutional Block Attention Module","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937220","doi":"https://doi.org/10.1109/icccnt56998.2023.10307210"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10307210","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt56998.2023.10307210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5092852397","display_name":"Mukul Kadaskar","orcid":"https://orcid.org/0000-0002-4931-2975"},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mukul Kadaskar","raw_affiliation_strings":["National Institute of Technology Karnataka,Department of Information Technology,Surathkal,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Karnataka,Department of Information Technology,Surathkal,India","institution_ids":["https://openalex.org/I11880225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040815462","display_name":"Nagamma Patil","orcid":"https://orcid.org/0000-0001-9924-9559"},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nagamma Patil","raw_affiliation_strings":["National Institute of Technology Karnataka,Department of Information Technology,Surathkal,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Karnataka,Department of Information Technology,Surathkal,India","institution_ids":["https://openalex.org/I11880225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I11880225"],"apc_list":null,"apc_paid":null,"fwci":0.4356,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.61252346,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"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/T10862","display_name":"AI in cancer detection","score":0.9997000098228455,"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":0.9997000098228455,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9965999722480774,"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/dice","display_name":"Dice","score":0.8339412212371826},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7804408073425293},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7144023776054382},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.7113024592399597},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5534095764160156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5401946306228638},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4768734574317932},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4478783905506134},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.424924373626709},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3991776704788208}],"concepts":[{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.8339412212371826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7804408073425293},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7144023776054382},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.7113024592399597},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5534095764160156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5401946306228638},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4768734574317932},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4478783905506134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.424924373626709},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3991776704788208},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10307210","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt56998.2023.10307210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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":25,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1970962264","https://openalex.org/W2062413904","https://openalex.org/W2592905743","https://openalex.org/W2806787491","https://openalex.org/W2884436604","https://openalex.org/W2885343725","https://openalex.org/W2914272101","https://openalex.org/W2923997689","https://openalex.org/W2944712585","https://openalex.org/W2955425717","https://openalex.org/W2956013636","https://openalex.org/W2963881378","https://openalex.org/W2999219213","https://openalex.org/W3012093480","https://openalex.org/W3026693286","https://openalex.org/W3136850285","https://openalex.org/W3145444543","https://openalex.org/W4223446115","https://openalex.org/W4289752563","https://openalex.org/W4308937272","https://openalex.org/W6631190155","https://openalex.org/W6762718338","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W3021239166","https://openalex.org/W2586273397","https://openalex.org/W4366341510","https://openalex.org/W2390936256","https://openalex.org/W2483429559","https://openalex.org/W2016385589","https://openalex.org/W2009559548","https://openalex.org/W1522196789","https://openalex.org/W4379141755"],"abstract_inverted_index":{"Cell":[0],"nuclei":[1],"segmentation":[2],"is":[3,13,78],"crucial":[4],"for":[5,91],"developing":[6],"digital":[7],"pathology":[8],"and":[9,21,41,59,82,117],"medical":[10,23,73],"research.":[11],"It":[12],"helpful":[14],"in":[15,64,72],"numerous":[16],"applications,":[17],"including":[18],"illness":[19],"diagnostics":[20],"other":[22],"therapies.":[24],"Unfortunately,":[25],"due":[26],"to":[27,46,68,80,86,102],"the":[28,47,52,65,88,99],"massive":[29],"number":[30],"of":[31,35],"nuclei,":[32],"manual":[33],"analysis":[34],"these":[36],"image":[37],"slides":[38],"takes":[39],"time":[40],"effort.":[42],"Morphometric":[43],"appearances":[44],"add":[45],"complexity.":[48],"Nevertheless,":[49],"we":[50],"upgraded":[51],"Nested":[53],"UNet":[54],"with":[55],"an":[56],"EfficientNetV2S":[57],"backbone":[58],"added":[60],"a":[61],"CBAM":[62],"module":[63],"decoder":[66],"layers":[67],"obtain":[69],"state-of-the-art":[70],"performance":[71],"imaging.":[74],"This":[75],"improved":[76],"model":[77,97,109],"easier":[79],"train,":[81],"attention":[83],"blocks":[84],"help":[85],"tune":[87],"retrieved":[89],"features":[90],"better":[92],"performance.":[93],"We":[94],"tested":[95],"our":[96],"using":[98],"CryoNuSeg":[100],"dataset":[101],"see":[103],"how":[104],"well":[105],"it":[106],"performed.":[107],"Our":[108],"scores":[110],"0.941":[111],"on":[112,115,119],"Dice,":[113],"0.605":[114],"AJI,":[116],"0.614":[118],"PQ.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
