{"id":"https://openalex.org/W3125832420","doi":"https://doi.org/10.1109/access.2021.3053408","title":"INet: Convolutional Networks for Biomedical Image Segmentation","display_name":"INet: Convolutional Networks for Biomedical Image Segmentation","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3125832420","doi":"https://doi.org/10.1109/access.2021.3053408","mag":"3125832420"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3053408","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053408","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330594.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330594.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002394415","display_name":"Weihao Weng","orcid":"https://orcid.org/0000-0002-0869-3409"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Weihao Weng","raw_affiliation_strings":["Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan"],"raw_orcid":"https://orcid.org/0000-0002-0869-3409","affiliations":[{"raw_affiliation_string":"Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105923667","display_name":"Xin Zhu","orcid":"https://orcid.org/0000-0002-4376-0806"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xin Zhu","raw_affiliation_strings":["Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":33.5732,"has_fulltext":true,"cited_by_count":498,"citation_normalized_percentile":{"value":0.99853264,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"16591","last_page":"16603"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T10862","display_name":"AI in cancer detection","score":0.9976999759674072,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9937999844551086,"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/upsampling","display_name":"Upsampling","score":0.8219888210296631},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8147772550582886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7383482456207275},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6463366150856018},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5750306844711304},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5737588405609131},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5335479378700256},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5220938920974731},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4457107186317444},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4405773878097534},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41033440828323364},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3801535964012146},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37148550152778625},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20320618152618408},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11714127659797668},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10376995801925659}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8219888210296631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8147772550582886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7383482456207275},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6463366150856018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5750306844711304},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5737588405609131},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5335479378700256},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5220938920974731},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4457107186317444},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4405773878097534},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41033440828323364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3801535964012146},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37148550152778625},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20320618152618408},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11714127659797668},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10376995801925659},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3053408","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053408","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330594.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:09f9159c061f490e9c57a3b562c052ea","is_oa":true,"landing_page_url":"https://doaj.org/article/09f9159c061f490e9c57a3b562c052ea","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 16591-16603 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3053408","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3053408","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09330594.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G1038631717","display_name":null,"funder_award_id":"2020-P-3","funder_id":"https://openalex.org/F4320324016","funder_display_name":"University of Aizu"},{"id":"https://openalex.org/G1910304728","display_name":"Study on ECG automatic analysis technology and its clinical usefulness based on artificial intelligence and cardiac simulation","funder_award_id":"18K11532","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4326201076","display_name":"DETECTION OF SESSILE SERRATED ADENOMA/POLYPS USING CONVOLUTIONAL NEURAL NETWORK","funder_award_id":"18K08010","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320324016","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3125832420.pdf","grobid_xml":"https://content.openalex.org/works/W3125832420.grobid-xml"},"referenced_works_count":71,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1817277359","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1932847118","https://openalex.org/W1989144816","https://openalex.org/W1994062553","https://openalex.org/W1998865404","https://openalex.org/W2025381747","https://openalex.org/W2097117768","https://openalex.org/W2101579926","https://openalex.org/W2109255472","https://openalex.org/W2112796928","https://openalex.org/W2134993189","https://openalex.org/W2139427956","https://openalex.org/W2142332605","https://openalex.org/W2144982973","https://openalex.org/W2163605009","https://openalex.org/W2182098131","https://openalex.org/W2194775991","https://openalex.org/W2288122362","https://openalex.org/W2304648132","https://openalex.org/W2307770531","https://openalex.org/W2406474429","https://openalex.org/W2517954747","https://openalex.org/W2556967412","https://openalex.org/W2559597482","https://openalex.org/W2586748964","https://openalex.org/W2586952804","https://openalex.org/W2598666589","https://openalex.org/W2621028221","https://openalex.org/W2626367459","https://openalex.org/W2630837129","https://openalex.org/W2774320778","https://openalex.org/W2884436604","https://openalex.org/W2888538030","https://openalex.org/W2900298334","https://openalex.org/W2910094941","https://openalex.org/W2912225506","https://openalex.org/W2915126261","https://openalex.org/W2916798096","https://openalex.org/W2919115771","https://openalex.org/W2928133111","https://openalex.org/W2943666912","https://openalex.org/W2949846184","https://openalex.org/W2962992847","https://openalex.org/W2963446712","https://openalex.org/W2963516899","https://openalex.org/W2963623257","https://openalex.org/W2963881378","https://openalex.org/W2963946669","https://openalex.org/W2964043069","https://openalex.org/W2964227007","https://openalex.org/W2964350391","https://openalex.org/W2969361965","https://openalex.org/W4285719527","https://openalex.org/W4309233581","https://openalex.org/W6637373629","https://openalex.org/W6638480814","https://openalex.org/W6639824700","https://openalex.org/W6684191040","https://openalex.org/W6694260854","https://openalex.org/W6697974390","https://openalex.org/W6713596413","https://openalex.org/W6729983426","https://openalex.org/W6739696289","https://openalex.org/W6753441378","https://openalex.org/W6755891590","https://openalex.org/W6758047254","https://openalex.org/W6758710848","https://openalex.org/W6759274242"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3004135429","https://openalex.org/W4386858688","https://openalex.org/W2982536526","https://openalex.org/W3121005460","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Encoder-decoder":[0],"networks":[1],"are":[2,27],"state-of-the-art":[3],"approaches":[4,261],"to":[5,82,145,157,233,286],"biomedical":[6,130,219,273],"image":[7,131,220,274],"segmentation,":[8,221],"but":[9,36],"have":[10],"two":[11,137,293],"problems:":[12],"i.e.,":[13,140],"the":[14,50,69,109,122,126,147,177,186,307],"widely":[15],"used":[16],"pooling":[17],"operations":[18],"may":[19],"discard":[20],"spatial":[21,51],"information,":[22],"and":[23,86,151,161,184,230,242,246,252,267,298,304],"therefore":[24],"low-level":[25,173],"semantics":[26,174],"lost.":[28],"Feature":[29],"fusion":[30],"methods":[31,295],"can":[32,171],"mitigate":[33],"these":[34,58],"problems":[35],"feature":[37,54,111,153,159,178],"maps":[38,112,154,160,179],"of":[39,53,72,92,104,113,180,284],"different":[40,105],"scales":[41],"cannot":[42],"be":[43],"easily":[44],"fused":[45],"because":[46],"downand":[47],"upsampling":[48],"change":[49],"resolution":[52],"map.":[55],"To":[56,211],"address":[57],"issues,":[59],"we":[60,198,222],"propose":[61],"INet,":[62],"which":[63,279],"enlarges":[64],"receptive":[65,288],"fields":[66],"by":[67,95,102,175,188],"increasing":[68],"kernel":[70,124],"sizes":[71,106],"convolutional":[73,116],"layers":[74,183],"in":[75,272],"steps":[76],"(e.g.,":[77],"from":[78],"3":[79,81],"\u00d7":[80,84,89],"7":[83,85],"then":[87],"15":[88],"15)":[90],"instead":[91,283],"downsampling.":[93],"Inspired":[94],"an":[96],"Inception":[97],"module,":[98],"INet":[99,135,156,170,193,200,213,227,241,266,276,290],"extracts":[100],"features":[101],"kernels":[103],"through":[107],"concatenating":[108,176],"output":[110],"all":[114,181],"preceding":[115,182],"layers.":[117,166],"We":[118],"also":[119,206,291],"find":[120],"that":[121,205,300],"large":[123],"makes":[125],"network":[127],"feasible":[128],"for":[129,218],"segmentation.":[132,275],"In":[133,167,255],"addition,":[134],"uses":[136],"overlapping":[138],"max-poolings,":[139],"max-poolings":[141],"with":[142,201,237,257],"stride":[143],"1,":[144],"extract":[146],"sharpest":[148],"features.":[149],"Fixed-size":[150],"fixed-channel":[152],"enable":[155],"concatenate":[158],"add":[162],"multiple":[163,190],"shortcuts":[164,209,239],"across":[165,309],"this":[168],"way,":[169],"recover":[172],"expedite":[185],"training":[187],"adding":[189],"shortcuts.":[191],"Because":[192],"has":[194,207],"additional":[195],"residual":[196,208,238],"shortcuts,":[197],"compare":[199,231],"a":[202,215,234],"UNet":[203],"system":[204,236],"(ResUNet).":[210],"confirm":[212],"as":[214],"backbone":[216],"architecture":[217],"implement":[223],"dense":[224],"connections":[225],"on":[226],"(called":[228],"DenseINet)":[229],"it":[232],"DenseUNet":[235],"(ResDenseUNet).":[240],"DenseINet":[243,268],"require":[244],"16.9%":[245],"37.6%":[247],"fewer":[248],"parameters":[249],"than":[250],"ResUNet":[251],"ResDenseUNet,":[253],"respectively.":[254],"comparison":[256],"six":[258],"encoder-":[259],"decoder":[260],"using":[262],"nine":[263],"public":[264],"datasets,":[265],"demonstrate":[269],"efficient":[270],"improvements":[271],"outperforms":[277,292],"DeepLabV3,":[278],"implementing":[280],"atrous":[281],"convolution":[282],"downsampling":[285],"increase":[287],"fields.":[289],"recent":[294],"(named":[296],"HRNet":[297],"MS-NAS)":[299],"maintain":[301],"high-resolution":[302],"representations":[303],"repeatedly":[305],"exchange":[306],"information":[308],"resolutions.":[310]},"counts_by_year":[{"year":2026,"cited_by_count":35},{"year":2025,"cited_by_count":117},{"year":2024,"cited_by_count":101},{"year":2023,"cited_by_count":124},{"year":2022,"cited_by_count":95},{"year":2021,"cited_by_count":26}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
