{"id":"https://openalex.org/W4387121767","doi":"https://doi.org/10.1145/3613307.3613320","title":"Tumor Segmentation in Weakly Paired Anatomical and Functional MRI images with Multimodal Information Fusion","display_name":"Tumor Segmentation in Weakly Paired Anatomical and Functional MRI images with Multimodal Information Fusion","publication_year":2023,"publication_date":"2023-07-21","ids":{"openalex":"https://openalex.org/W4387121767","doi":"https://doi.org/10.1145/3613307.3613320"},"language":"en","primary_location":{"id":"doi:10.1145/3613307.3613320","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3613307.3613320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 8th International Conference on Biomedical Signal and 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/A5103137270","display_name":"Pei Li Joe Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pei Zhou","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073025681","display_name":"Houjin Chen","orcid":"https://orcid.org/0000-0002-9247-8495"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houjin Chen","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653133","display_name":"Yanfeng Li","orcid":"https://orcid.org/0000-0002-8441-7721"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanfeng Li","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089537405","display_name":"Yahui Peng","orcid":"https://orcid.org/0000-0002-2520-1170"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yahui Peng","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103137270"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30713758,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"66","last_page":"71"},"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":0.9991999864578247,"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":0.9991999864578247,"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":0.9983999729156494,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7899912595748901},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6999436616897583},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.680188775062561},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6280077695846558},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.6224568486213684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5135110020637512},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48870208859443665},{"id":"https://openalex.org/keywords/lung-cancer","display_name":"Lung cancer","score":0.4186426103115082},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3553433120250702},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2955939769744873},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1872994303703308},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.10113468766212463}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7899912595748901},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6999436616897583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680188775062561},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6280077695846558},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.6224568486213684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5135110020637512},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48870208859443665},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.4186426103115082},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3553433120250702},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2955939769744873},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1872994303703308},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.10113468766212463}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3613307.3613320","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3613307.3613320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 8th International Conference on Biomedical Signal and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2884585870","https://openalex.org/W3163925244","https://openalex.org/W4200014273","https://openalex.org/W4211236050","https://openalex.org/W4361029986","https://openalex.org/W4385586966"],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W2374013449","https://openalex.org/W73545470","https://openalex.org/W2364381299","https://openalex.org/W2374430585","https://openalex.org/W3144423903","https://openalex.org/W2377397762","https://openalex.org/W2793967660","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Multimodal":[0],"magnetic":[1],"resonance":[2],"imaging":[3],"(MRI)":[4],"contains":[5],"complementary":[6],"information":[7],"in":[8,28,70,89,106],"anatomical":[9,72,92,184],"and":[10,18,36,73,93,137,190],"functional":[11,74,94,194],"images":[12,47,76],"that":[13,170],"help":[14,32,134],"the":[15,104,112,123,135,145,175,181,191],"accurate":[16,67],"diagnosis":[17],"treatment":[19],"evaluation":[20],"of":[21,140,156,161,178,183,193],"lung":[22,59,71,86,149,164],"cancers.":[23],"Accurately":[24],"segmenting":[25],"tumor":[26,68,87,150,208],"regions":[27],"each":[29,179],"modality":[30,119],"can":[31],"to":[33,55,65,102,115,117,133,188,198],"obtain":[34,66],"comprehensive":[35],"precise":[37],"information.":[38,142],"Existing":[39],"multimodal":[40,130,207],"segmentation":[41,69,88,113,151,176,209],"methods":[42,210],"are":[43],"mostly":[44],"used":[45],"for":[46,58,85],"with":[48,152,205],"strict":[49],"registration,":[50],"but":[51],"it":[52,57],"is":[53,63,186,196,201],"difficult":[54],"achieve":[56],"MRI":[60,75,95,159,185,195],"images.":[61,96],"It":[62],"challenging":[64],"simultaneously.":[77,121],"In":[78],"this":[79,171],"paper,":[80],"we":[81,98,126],"propose":[82],"a":[83,128,153],"method":[84,147,172],"weakly":[90],"paired":[91],"Firstly,":[97],"use":[99],"domain":[100],"adaptation":[101],"narrow":[103],"gap":[105],"features":[107],"across":[108],"different":[109,118],"modalities,":[110],"enabling":[111],"network":[114],"adapt":[116],"data":[120],"At":[122],"same":[124],"time,":[125],"explore":[127],"two-stage":[129],"co-attention":[131],"mechanism":[132],"extraction":[136],"effective":[138],"fusion":[139],"multi-modal":[141],"We":[143],"evaluate":[144],"proposed":[146],"on":[148],"clinical":[154],"dataset":[155],"90":[157],"chest":[158],"scans":[160],"non-small":[162],"cell":[163],"cancer":[165],"(NSCLC).":[166],"The":[167],"results":[168],"show":[169],"effectively":[173],"improves":[174],"accuracy":[177],"modality,":[180],"DSC":[182,192],"increased":[187,197],"0.81\u00b10.19,":[189],"0.77\u00b10.23,":[199],"which":[200],"significantly":[202],"improved":[203],"compared":[204],"several":[206],"(p":[211],"<0.05).":[212]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}