{"id":"https://openalex.org/W3160842135","doi":"https://doi.org/10.1109/icpr48806.2021.9411976","title":"DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume","display_name":"DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160842135","doi":"https://doi.org/10.1109/icpr48806.2021.9411976","mag":"3160842135"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9411976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9411976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/A5100446997","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0002-8759-4811"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["AI Lab, Lenovo Research, Beijing, China","Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101106695","display_name":"Jiang Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Tian","raw_affiliation_strings":["AI Lab, Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101411299","display_name":"Cheng Zhong","orcid":"https://orcid.org/0000-0002-6886-7319"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Zhong","raw_affiliation_strings":["AI Lab, Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100354683","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0002-4170-4798"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["AI Lab, Lenovo Research, Beijing, China","Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101873354","display_name":"Zhongchao Shi","orcid":"https://orcid.org/0000-0002-5216-3827"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongchao Shi","raw_affiliation_strings":["AI Lab, Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100689909","display_name":"Zhiqiang He","orcid":"https://orcid.org/0000-0001-8882-6605"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang He","raw_affiliation_strings":["Lenovo Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Lenovo Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100446997"],"corresponding_institution_ids":["https://openalex.org/I4210156165"],"apc_list":null,"apc_paid":null,"fwci":0.5806,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.67641664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"7796","last_page":"7803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9959999918937683,"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":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7980389595031738},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7960069179534912},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7880107164382935},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7462262511253357},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7173181176185608},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6474536657333374},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5215566158294678},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44561246037483215}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7980389595031738},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7960069179534912},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7880107164382935},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7462262511253357},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7173181176185608},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6474536657333374},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5215566158294678},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44561246037483215},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9411976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9411976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2109255472","https://openalex.org/W2153431772","https://openalex.org/W2168894214","https://openalex.org/W2194775991","https://openalex.org/W2195702343","https://openalex.org/W2412782625","https://openalex.org/W2502312327","https://openalex.org/W2526009326","https://openalex.org/W2559597482","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2598666589","https://openalex.org/W2613041730","https://openalex.org/W2630837129","https://openalex.org/W2752782242","https://openalex.org/W2799166040","https://openalex.org/W2888358068","https://openalex.org/W2889646458","https://openalex.org/W2910094941","https://openalex.org/W2936835627","https://openalex.org/W2941137223","https://openalex.org/W2952234052","https://openalex.org/W2955058313","https://openalex.org/W2962891704","https://openalex.org/W2963091558","https://openalex.org/W2963727650","https://openalex.org/W2963881378","https://openalex.org/W2964121744","https://openalex.org/W2964227007","https://openalex.org/W2965669393","https://openalex.org/W2970971581","https://openalex.org/W2979839221","https://openalex.org/W2997225633","https://openalex.org/W3008464434","https://openalex.org/W3102875249","https://openalex.org/W3104211200","https://openalex.org/W4295312788","https://openalex.org/W4309233581","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6676338569","https://openalex.org/W6684665197","https://openalex.org/W6724804524","https://openalex.org/W6739696289","https://openalex.org/W6739901393","https://openalex.org/W6751733626","https://openalex.org/W6758047254","https://openalex.org/W6761320662","https://openalex.org/W6774462951"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Automatic":[0],"liver":[1,24,49,73],"tumor":[2,25,35,50,74],"segmentation":[3,26,51,75,209],"from":[4,76,95],"3D":[5],"Computed":[6],"Tomography":[7],"(CT)":[8],"is":[9],"a":[10,65,120,144],"necessary":[11],"prerequisite":[12],"in":[13,48,89,112,132,155],"the":[14,31,104,137,160,171],"interventions":[15],"of":[16,34,55,92,106,109,115,139,162],"hepatic":[17],"abnormalities":[18],"and":[19,37,84,143,182,199],"surgery":[20],"planning.":[21],"However,":[22],"accurate":[23],"remains":[27],"challenging":[28],"due":[29],"to":[30,102,126,150,195,203,205],"large":[32],"variability":[33],"sizes":[36],"inhomogeneous":[38],"texture.":[39],"Recent":[40],"advances":[41],"based":[42],"on":[43,53,170],"Fully":[44],"Convolutional":[45],"Network":[46,69],"(FCN)":[47],"draw":[52],"success":[54],"learning":[56],"discriminative":[57],"multi-level":[58,197],"features.":[59],"In":[60],"this":[61],"paper,":[62],"we":[63,98,118],"propose":[64],"Deep":[66],"Attentive":[67],"Refinement":[68,123,147],"(DARN)":[70],"for":[71],"improved":[72],"CT":[77],"volumes":[78],"by":[79],"fully":[80],"exploiting":[81],"both":[82],"low":[83,133,163],"high":[85,140,156],"level":[86,134,141,157,164],"features":[87,110,135,158,198],"embedded":[88],"different":[90,107,113],"layers":[91,114],"FCN.":[93,116],"Different":[94],"existing":[96],"works,":[97],"exploit":[99,196],"attention":[100],"mechanism":[101],"leverage":[103],"relation":[105],"levels":[108],"encoded":[111],"Specifically,":[117],"introduce":[119],"Semantic":[121],"Attention":[122,146],"(SemRef)":[124],"module":[125,149],"selectively":[127],"emphasize":[128],"global":[129],"semantic":[130],"information":[131],"with":[136,159],"guidance":[138,161],"ones,":[142],"Spatial":[145],"(SpaRef)":[148],"adaptively":[151],"enhance":[152],"spatial":[153],"details":[154],"ones.":[165],"We":[166],"evaluate":[167],"our":[168],"network":[169],"public":[172],"MICCAI":[173],"2017":[174],"Liver":[175],"Tumor":[176],"Segmentation":[177],"Challenge":[178],"dataset":[179],"(LiTS":[180],"dataset)":[181],"it":[183],"achieves":[184],"state-of-the-art":[185],"performance.":[186],"The":[187],"proposed":[188],"refinement":[189],"modules":[190],"are":[191],"an":[192],"effective":[193],"strategy":[194],"has":[200],"great":[201],"potential":[202],"generalize":[204],"other":[206],"medical":[207],"image":[208],"tasks.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}
