{"id":"https://openalex.org/W3009707803","doi":"https://doi.org/10.1109/access.2020.2975983","title":"HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation","display_name":"HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3009707803","doi":"https://doi.org/10.1109/access.2020.2975983","mag":"3009707803"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2975983","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2975983","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09007452.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/ielx7/6287639/8948470/09007452.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043984072","display_name":"Chengzhi Lyu","orcid":"https://orcid.org/0000-0001-9160-0324"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengzhi Lyu","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9160-0324","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023248700","display_name":"Guoqing Hu","orcid":"https://orcid.org/0000-0001-6526-2560"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqing Hu","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6526-2560","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100411808","display_name":"Dan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Wang","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3272-9045","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043984072"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9396,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77741821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"8","issue":null,"first_page":"38210","last_page":"38220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9986000061035156,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.859958291053772},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.712106466293335},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7008644342422485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6115002632141113},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5590944290161133},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5139662623405457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4847797751426697},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.46704214811325073},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.46685534715652466},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45388489961624146},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42016831040382385}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.859958291053772},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.712106466293335},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7008644342422485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6115002632141113},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5590944290161133},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5139662623405457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4847797751426697},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.46704214811325073},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.46685534715652466},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45388489961624146},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42016831040382385},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2975983","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2975983","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09007452.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:31566aed9cad4bbe854c3aaa0419a482","is_oa":true,"landing_page_url":"https://doaj.org/article/31566aed9cad4bbe854c3aaa0419a482","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 8, Pp 38210-38220 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2975983","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2975983","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09007452.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3009707803.pdf","grobid_xml":"https://content.openalex.org/works/W3009707803.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1937937026","https://openalex.org/W1971483412","https://openalex.org/W2022508996","https://openalex.org/W2040520535","https://openalex.org/W2097117768","https://openalex.org/W2110015572","https://openalex.org/W2129523948","https://openalex.org/W2139427956","https://openalex.org/W2145305441","https://openalex.org/W2150769593","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2206167351","https://openalex.org/W2258978862","https://openalex.org/W2302255633","https://openalex.org/W2306289963","https://openalex.org/W2320230300","https://openalex.org/W2327793514","https://openalex.org/W2442791716","https://openalex.org/W2559597482","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2565639579","https://openalex.org/W2575748305","https://openalex.org/W2579318141","https://openalex.org/W2583914614","https://openalex.org/W2600966010","https://openalex.org/W2606492274","https://openalex.org/W2606663706","https://openalex.org/W2788906943","https://openalex.org/W2791680898","https://openalex.org/W2792110043","https://openalex.org/W2809144587","https://openalex.org/W2886773299","https://openalex.org/W2890215949","https://openalex.org/W2896194744","https://openalex.org/W2898910301","https://openalex.org/W2901364394","https://openalex.org/W2921406441","https://openalex.org/W2921948732","https://openalex.org/W2922209188","https://openalex.org/W2927980542","https://openalex.org/W2941942490","https://openalex.org/W2949117887","https://openalex.org/W2950321108","https://openalex.org/W2952222533","https://openalex.org/W2962850830","https://openalex.org/W2963881378","https://openalex.org/W2964227007","https://openalex.org/W2964250774","https://openalex.org/W2964273387","https://openalex.org/W3103010481","https://openalex.org/W4294226146","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6698183232","https://openalex.org/W6733344817","https://openalex.org/W6755808572","https://openalex.org/W6764133899"],"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/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W4205800335","https://openalex.org/W2758994127","https://openalex.org/W2386644571"],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,32,48,155],"of":[2,17,99,130],"fine-grained":[3,46,100,138],"information":[4,66,133],"is":[5,50,134],"an":[6],"important":[7],"step":[8],"in":[9,30,121,145,150],"medical":[10],"image":[11,47],"analysis":[12],"applications.":[13],"With":[14],"the":[15,18,69,79,96,105,124,128,146],"development":[16],"encoder-decoder-based":[19],"networks,":[20],"various":[21],"network":[22,43],"structures":[23],"and":[24,71,115,148,153],"algorithms":[25],"have":[26],"made":[27],"significant":[28,143],"progress":[29],"semantic":[31],"tasks.":[33,156],"This":[34],"work":[35],"aims":[36],"to":[37,63,77,94,123],"present":[38],"a":[39,58,73,90,110],"novel":[40],"high-resolution":[41],"encoder-decoder":[42],"(HRED-Net)":[44],"for":[45,53,137],"that":[49,120],"highly":[51],"accurate":[52],"small-scale":[54],"targets.":[55],"We":[56],"design":[57],"multiscale":[59,126],"context":[60],"connection":[61],"module":[62],"extract":[64],"feature":[65],"without":[67],"reducing":[68],"resolution,":[70],"propose":[72],"multiresolution":[74,131],"fusion":[75,129],"model":[76],"fine-tune":[78],"final":[80],"results.":[81],"In":[82],"addition,":[83],"these":[84,113],"modules":[85],"are":[86],"trained":[87],"together":[88],"with":[89],"detail-oriented":[91],"loss":[92],"function":[93],"enhance":[95],"model's":[97],"perception":[98],"parts.":[101],"Through":[102],"experiments":[103],"on":[104],"DRIVE":[106],"dataset,":[107],"we":[108],"found":[109],"balance":[111],"between":[112],"modules,":[114],"our":[116],"comparison":[117],"results":[118],"show":[119],"addition":[122],"extraction":[125],"features,":[127],"prediction":[132],"also":[135],"beneficial":[136],"segmentation.":[139],"Our":[140],"method":[141],"yielded":[142],"improvements":[144],"accuracy":[147],"sensitivity":[149],"retinal":[151],"vessel":[152],"lung":[154]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}