{"id":"https://openalex.org/W2962951499","doi":"https://doi.org/10.1109/isbi.2019.8759555","title":"Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation","display_name":"Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2962951499","doi":"https://doi.org/10.1109/isbi.2019.8759555","mag":"2962951499"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","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/A5088691885","display_name":"Xueying Chen","orcid":"https://orcid.org/0000-0002-5167-3550"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xueying Chen","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101618647","display_name":"Rong Zhang","orcid":"https://orcid.org/0000-0002-8136-0643"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zhang","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054075262","display_name":"Pingkun Yan","orcid":"https://orcid.org/0000-0002-9779-2141"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pingkun Yan","raw_affiliation_strings":["Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088691885"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":7.8815,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97905336,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"430","last_page":"433"},"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":0.998199999332428,"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":0.998199999332428,"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/T10036","display_name":"Advanced Neural Network Applications","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/T10862","display_name":"AI in cancer detection","score":0.996999979019165,"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/upsampling","display_name":"Upsampling","score":0.8848997354507446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8099424839019775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7619545459747314},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6444034576416016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5879946351051331},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5546556115150452},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5366854667663574},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5253264904022217},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5015780925750732},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.47303956747055054},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.43601173162460327},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4317968487739563},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42273572087287903},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42185813188552856},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4022265672683716},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28253188729286194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25103288888931274},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.0792078971862793}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8848997354507446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8099424839019775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7619545459747314},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6444034576416016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5879946351051331},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5546556115150452},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5366854667663574},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5253264904022217},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5015780925750732},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.47303956747055054},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.43601173162460327},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4317968487739563},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42273572087287903},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42185813188552856},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4022265672683716},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28253188729286194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25103288888931274},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0792078971862793},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2019.8759555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2463818697","https://openalex.org/W2476548250","https://openalex.org/W2522861252","https://openalex.org/W2526009326","https://openalex.org/W2563705555","https://openalex.org/W2609314062","https://openalex.org/W2752782242","https://openalex.org/W2769910914","https://openalex.org/W2771088639","https://openalex.org/W2790296028","https://openalex.org/W2897652402","https://openalex.org/W2951123255","https://openalex.org/W2952234052","https://openalex.org/W2963420686","https://openalex.org/W2963815618","https://openalex.org/W2964227007","https://openalex.org/W3101123465","https://openalex.org/W4297748429","https://openalex.org/W6639204139","https://openalex.org/W6639824700","https://openalex.org/W6719642423","https://openalex.org/W6727911400","https://openalex.org/W6736541308","https://openalex.org/W6743731764","https://openalex.org/W6746189028","https://openalex.org/W6749996660","https://openalex.org/W6766499329","https://openalex.org/W6948015715"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4205800335","https://openalex.org/W2055202857","https://openalex.org/W2022929107","https://openalex.org/W80586315","https://openalex.org/W2758994127"],"abstract_inverted_index":{"Liver":[0,147],"lesion":[1,87],"segmentation":[2,18,78,88],"is":[3,96],"a":[4,71,126,131],"difficult":[5],"yet":[6],"critical":[7],"task":[8],"for":[9,118],"medical":[10],"image":[11,17,113],"analysis.":[12],"Recently,":[13],"deep":[14],"learning":[15],"based":[16,35,76,97],"methods":[19,46,54],"have":[20,48],"achieved":[21,153],"promising":[22],"performance,":[23],"which":[24,102],"can":[25,47],"be":[26],"divided":[27],"into":[28],"three":[29],"categories:":[30],"2D,":[31],"2.5D":[32,43],"and":[33,44,52,130,152],"3D,":[34],"on":[36,98,141],"the":[37,40,82,99,119,123,142],"dimensionality":[38],"of":[39,85,144],"models.":[41],"However,":[42],"3D":[45],"very":[49],"high":[50],"complexity":[51],"2D":[53,77],"may":[55],"not":[56],"perform":[57],"satisfactorily.":[58],"To":[59],"obtain":[60],"competitive":[61,154],"performance":[62],"with":[63,109,157],"low":[64],"complexity,":[65],"in":[66],"this":[67],"paper,":[68],"we":[69],"propose":[70],"Feature-fusion":[72],"Encoder-Decoder":[73],"Network":[74],"(FED-Net)":[75],"model":[79],"to":[80,116],"tackle":[81],"challenging":[83],"problem":[84],"liver":[86],"from":[89],"CT":[90],"images.":[91],"Our":[92],"feature":[93],"fusion":[94],"method":[95,140],"attention":[100],"mechanism,":[101],"fuses":[103],"high-level":[104],"features":[105,111],"carrying":[106],"semantic":[107],"information":[108,120],"low-level":[110],"having":[112],"details.":[114],"Additionally,":[115],"compensate":[117],"loss":[121],"during":[122],"upsampling":[124,128],"process,":[125],"dense":[127],"convolution":[129],"residual":[132],"convolutional":[133],"structure":[134],"are":[135],"proposed.":[136],"We":[137],"tested":[138],"our":[139],"dataset":[143],"MICCAI":[145],"2017":[146],"Tumor":[148],"Segmentation":[149],"(LiTS)":[150],"Challenge":[151],"results":[155],"compared":[156],"other":[158],"state-of-the-art":[159],"methods.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
