{"id":"https://openalex.org/W4406259609","doi":"https://doi.org/10.1109/bibm62325.2024.10821855","title":"Graph-guided Source Selection with Sequential Transfer for Medical Image Segmentation","display_name":"Graph-guided Source Selection with Sequential Transfer for Medical Image Segmentation","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406259609","doi":"https://doi.org/10.1109/bibm62325.2024.10821855"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10821855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10821855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5101861092","display_name":"Jingyun Yang","orcid":"https://orcid.org/0000-0001-7506-9895"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyun Yang","raw_affiliation_strings":["Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765087","display_name":"Guoqing Zhang","orcid":"https://orcid.org/0000-0002-1755-534X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqing Zhang","raw_affiliation_strings":["Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007773259","display_name":"Jingge Wang","orcid":"https://orcid.org/0000-0002-9231-882X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingge Wang","raw_affiliation_strings":["Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100421251","display_name":"Yang Li","orcid":"https://orcid.org/0000-0001-5260-5157"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101861092"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26078075,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2754","last_page":"2759"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9430999755859375,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9430999755859375,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9265000224113464,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9264000058174133,"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":0.7443036437034607},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5982420444488525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5082063674926758},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47504329681396484},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.46754851937294006},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44815006852149963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3911184072494507},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3649769425392151},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2804017663002014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7443036437034607},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5982420444488525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5082063674926758},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47504329681396484},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.46754851937294006},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44815006852149963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3911184072494507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3649769425392151},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2804017663002014}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10821855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10821855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2010365091","https://openalex.org/W2133665775","https://openalex.org/W2560647685","https://openalex.org/W2751069891","https://openalex.org/W2807841289","https://openalex.org/W2912989244","https://openalex.org/W2952735543","https://openalex.org/W2998705539","https://openalex.org/W3038918411","https://openalex.org/W3095986980","https://openalex.org/W3110409447","https://openalex.org/W3128427457","https://openalex.org/W3204365955","https://openalex.org/W3216470025","https://openalex.org/W4302009885","https://openalex.org/W4313413341","https://openalex.org/W4379925208","https://openalex.org/W4387211281","https://openalex.org/W4387211459","https://openalex.org/W4391547566","https://openalex.org/W6755715394","https://openalex.org/W6774108707","https://openalex.org/W6791619219","https://openalex.org/W6795090635","https://openalex.org/W6798128096","https://openalex.org/W6801769783","https://openalex.org/W6810212174","https://openalex.org/W6873608013"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2501551404","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Transfer":[0],"learning":[1,69,209],"is":[2,26],"a":[3,40,76,101,112,143,148,212],"critical":[4],"technique":[5],"in":[6,29,194,230],"training":[7],"deep":[8],"neural":[9],"networks":[10],"to":[11,70,81,152],"tackle":[12],"downstream":[13],"tasks":[14,58],"with":[15,86],"better":[16],"or":[17],"faster":[18],"solutions,":[19],"by":[20,118,155,211,218],"leveraging":[21],"previously":[22],"acquired":[23],"knowledge.":[24],"This":[25],"especially":[27],"useful":[28],"the":[30,48,53,83,93,106,119,123,132,136,153,164,172,176,188,191,196],"low-resource":[31],"medical":[32,71,138,185],"image":[33,72],"analysis":[34],"field.":[35],"However,":[36],"transferring":[37],"knowledge":[38],"from":[39],"less":[41],"related":[42],"source":[43,57,78,85,109,134,167,198],"task":[44,114],"can":[45],"negatively":[46],"impact":[47],"target":[49,95,137,154,177],"task\u2019s":[50],"performance,":[51],"making":[52],"selection":[54,79],"of":[55,127,166,190,232],"appropriate":[56],"vital.":[59],"To":[60,141],"make":[61],"up":[62],"for":[63,92,135,220,224,228],"its":[64],"deficiency":[65],"when":[66],"applying":[67],"transfer":[68,90,150,158,173,208],"segmentation,":[73],"we":[74,98,121,146,169],"propose":[75],"novel":[77],"framework":[80,193],"identify":[82,131],"landmark":[84,133],"an":[87],"effective":[88],"sequential":[89,149],"path":[91,151],"given":[94],"task.":[96,140,178],"Specifically,":[97],"first":[99],"construct":[100],"comprehensive":[102],"graph":[103],"that":[104,203],"reflects":[105],"relatedness":[107],"among":[108],"tasks,":[110,168],"using":[111],"medical-image-tailored":[113],"affinity":[115],"metric.":[116],"Guided":[117],"graph,":[120],"assess":[122],"informativeness":[124],"and":[125,130,159,226],"representativeness":[126],"each":[128],"node":[129],"segmentation":[139,233],"ensure":[142],"positive":[144],"transfer,":[145],"pinpoint":[147],"minimizing":[156],"both":[157],"search":[160],"costs.":[161],"By":[162],"optimizing":[163],"use":[165],"consequently":[170],"improve":[171],"performance":[174,217],"on":[175,181],"Extensive":[179],"experiments":[180],"three":[182],"brain":[183],"MRI":[184],"datasets":[186],"demonstrate":[187],"efficacy":[189],"proposed":[192],"finding":[195],"best":[197],"sequence.":[199],"The":[200],"results":[201],"show":[202],"our":[204],"method":[205],"outperforms":[206],"other":[207],"approaches":[210],"considerable":[213],"margin,":[214],"improving":[215],"state-of-the-art":[216],"6.61%":[219],"FeTS":[221],"2022,":[222],"0.66%":[223],"iSeg-2019,":[225],"1.70%":[227],"WMH":[229],"terms":[231],"Dice":[234],"score.":[235]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}