{"id":"https://openalex.org/W3012380425","doi":"https://doi.org/10.1117/12.2548670","title":"Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images","display_name":"Fully convolutional network with sparse feature-maps composition for automatic lung tumor segmentation from PET images","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W3012380425","doi":"https://doi.org/10.1117/12.2548670","mag":"3012380425"},"language":"en","primary_location":{"id":"doi:10.1117/12.2548670","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2548670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","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/A5045518534","display_name":"Haihong Tian","orcid":"https://orcid.org/0000-0002-7052-9239"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haihong Tian","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031688232","display_name":"Dehui Xiang","orcid":"https://orcid.org/0000-0001-7873-9778"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dehui Xiang","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058024483","display_name":"Weifang Zhu","orcid":"https://orcid.org/0000-0001-9540-4101"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifang Zhu","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048091475","display_name":"Fei Shi","orcid":"https://orcid.org/0000-0002-8878-6655"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Shi","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079807652","display_name":"Xinjian Chen","orcid":"https://orcid.org/0000-0002-0871-293X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjian Chen","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5045518534"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.2203,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55834647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"59","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9973999857902527,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9973999857902527,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9950000047683716,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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":0.7655948400497437},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6946974396705627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6937066316604614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5996964573860168},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5930148959159851},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5761861801147461},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5761300325393677},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5212634205818176},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4645163118839264},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4155891537666321}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7655948400497437},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6946974396705627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6937066316604614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5996964573860168},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5930148959159851},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5761861801147461},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5761300325393677},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5212634205818176},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4645163118839264},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4155891537666321},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2548670","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2548670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.550000011920929,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Accurate":[0],"lung":[1,14,33,52],"tumor":[2,15],"delineation":[3],"plays":[4],"an":[5,152],"important":[6],"role":[7],"in":[8,19,166],"radiotherapy":[9],"treatment":[10],"planning.":[11],"Since":[12],"the":[13,37,46,51,55,138,158,167],"has":[16],"poor":[17],"boundary":[18],"positron":[20],"emission":[21],"tomography":[22],"(PET)":[23],"images,":[24],"it":[25,96],"is":[26,107,148],"a":[27,68,76,130,142,182],"challenging":[28],"task":[29],"to":[30,88,109,136],"accurately":[31],"segment":[32],"tumor.":[34],"In":[35,63,125],"addition,":[36,126],"heart,":[38],"liver,":[39],"bones":[40],"and":[41,70,81,93,113,120,163,186],"other":[42],"tissues":[43],"generally":[44],"have":[45,59,170],"similar":[47],"gray":[48],"value":[49],"as":[50],"tumor,":[53],"therefore":[54],"segmentation":[56,174],"results":[57,175],"usually":[58],"high":[60],"false":[61],"positive.":[62],"this":[64],"paper,":[65],"we":[66,94,127],"propose":[67],"novel":[69],"efficient":[71],"fully":[72,97],"convolutional":[73,98],"network":[74,99],"with":[75,85,100,133,181],"trainable":[77],"compressed":[78,143],"sensing":[79,144],"module":[80],"deep":[82,129],"supervision":[83,131],"mechanism":[84,132,147],"sparse":[86,101,134],"constraints":[87,135],"comprehensively":[89],"address":[90],"these":[91],"challenges;":[92],"call":[95],"feature-maps":[102],"composition":[103],"(SFC-FCN).":[104],"Our":[105],"SFC-FCN":[106],"able":[108],"conduct":[110],"end-to-end":[111],"learning":[112],"inference,":[114],"compress":[115],"redundant":[116],"features":[117,139],"within":[118],"channels":[119],"extract":[121],"key":[122],"uncorrelated":[123],"features.":[124],"use":[128],"guide":[137],"extraction":[140],"by":[141,150],"module.":[145],"The":[146],"developed":[149],"driving":[151],"objective":[153],"function":[154],"that":[155,177],"directly":[156],"guides":[157],"training":[159],"of":[160,178],"both":[161],"lower":[162],"upper":[164],"layers":[165],"network.":[168],"We":[169],"achieved":[171],"more":[172],"accurate":[173],"than":[176],"state-of-the-art":[179],"approaches":[180],"much":[183,187],"faster":[184],"speed":[185],"fewer":[188],"parameters.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}