{"id":"https://openalex.org/W7123556011","doi":"https://doi.org/10.1049/ipr2.70274","title":"Multi\u2010Head Convolution Module With Dynamic Feature Fusion for Medical Image Segmentation","display_name":"Multi\u2010Head Convolution Module With Dynamic Feature Fusion for Medical Image Segmentation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123556011","doi":"https://doi.org/10.1049/ipr2.70274"},"language":"en","primary_location":{"id":"doi:10.1049/ipr2.70274","is_oa":true,"landing_page_url":"https://doi.org/10.1049/ipr2.70274","pdf_url":null,"source":{"id":"https://openalex.org/S83215360","display_name":"IET Image Processing","issn_l":"1751-9659","issn":["1751-9659","1751-9667"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1049/ipr2.70274","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122961491","display_name":"Zijian Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zijian Chen","raw_affiliation_strings":["College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China"],"raw_orcid":"https://orcid.org/0009-0004-1358-9982","affiliations":[{"raw_affiliation_string":"College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China","institution_ids":["https://openalex.org/I190242452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020792351","display_name":"Jiangwei Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangwei Qin","raw_affiliation_strings":["College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China","institution_ids":["https://openalex.org/I190242452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114063867","display_name":"Zhaohan Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohan Cai","raw_affiliation_strings":["College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China","institution_ids":["https://openalex.org/I190242452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054562248","display_name":"Zhanpeng Huang","orcid":"https://orcid.org/0000-0003-1888-7490"},"institutions":[{"id":"https://openalex.org/I190242452","display_name":"Guangdong Pharmaceutical University","ror":"https://ror.org/02vg7mz57","country_code":"CN","type":"education","lineage":["https://openalex.org/I190242452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanpeng Huang","raw_affiliation_strings":["College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China"],"raw_orcid":"https://orcid.org/0000-0003-1888-7490","affiliations":[{"raw_affiliation_string":"College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China","institution_ids":["https://openalex.org/I190242452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5122961491"],"corresponding_institution_ids":["https://openalex.org/I190242452"],"apc_list":{"value":2000,"currency":"EUR","value_usd":2200},"apc_paid":{"value":2000,"currency":"EUR","value_usd":2200},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05993825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.42100000381469727,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.42100000381469727,"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":0.057100001722574234,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.03280000016093254,"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":0.6407999992370605},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6175000071525574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6025999784469604},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.579800009727478},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5673999786376953},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.527899980545044},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46000000834465027},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4440000057220459},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4171999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797999978065491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7342000007629395},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6407999992370605},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6175000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6025999784469604},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.579800009727478},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5673999786376953},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45910000801086426},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4440000057220459},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.39890000224113464},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1049/ipr2.70274","is_oa":true,"landing_page_url":"https://doi.org/10.1049/ipr2.70274","pdf_url":null,"source":{"id":"https://openalex.org/S83215360","display_name":"IET Image Processing","issn_l":"1751-9659","issn":["1751-9659","1751-9667"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Image Processing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1049/ipr2.70274","is_oa":true,"landing_page_url":"https://doi.org/10.1049/ipr2.70274","pdf_url":null,"source":{"id":"https://openalex.org/S83215360","display_name":"IET Image Processing","issn_l":"1751-9659","issn":["1751-9659","1751-9667"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Image Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2008359794","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2752782242","https://openalex.org/W2804047627","https://openalex.org/W2888358068","https://openalex.org/W2910094941","https://openalex.org/W2922509574","https://openalex.org/W2928165649","https://openalex.org/W2963091558","https://openalex.org/W3034421924","https://openalex.org/W3131500599","https://openalex.org/W3138516171","https://openalex.org/W3139633126","https://openalex.org/W3151130473","https://openalex.org/W3204614423","https://openalex.org/W4214493665","https://openalex.org/W4214709605","https://openalex.org/W4307726656","https://openalex.org/W4312290555","https://openalex.org/W4312950730","https://openalex.org/W4313170858","https://openalex.org/W4319300502","https://openalex.org/W4386076222","https://openalex.org/W4390872550","https://openalex.org/W4390872693","https://openalex.org/W4392845143","https://openalex.org/W4396528802","https://openalex.org/W4396677248","https://openalex.org/W4396918399","https://openalex.org/W4399383175","https://openalex.org/W4402452207","https://openalex.org/W4402742544","https://openalex.org/W4403088538","https://openalex.org/W4408724431"],"related_works":[],"abstract_inverted_index":{"ABSTRACT":[0],"Transformers":[1],"have":[2],"achieved":[3],"remarkable":[4],"success":[5],"in":[6,162],"medical":[7,164],"image":[8,165],"segmentation":[9,166,171],"due":[10],"to":[11,34,92,98,110,126],"their":[12,32],"large":[13],"receptive":[14,61],"fields":[15],"and":[16,63,67,104,120,176],"global":[17,82,99],"context":[18],"extraction":[19,57],"capabilities.":[20],"However,":[21],"convolutional":[22],"neural":[23],"networks":[24],"(CNNs)":[25],"face":[26],"limitations":[27],"with":[28],"fixed\u2010size":[29],"kernels,":[30],"hindering":[31],"ability":[33],"capture":[35],"multi\u2010scale":[36],"features.":[37],"To":[38],"address":[39],"these":[40],"challenges,":[41],"we":[42,85,131,148],"propose":[43],"two":[44],"novel":[45],"modules:":[46],"the":[47,60,68,87,94,112,118,128,134,145,150,168,177],"multi\u2010head":[48,113,135],"channel":[49,65],"mixed":[50],"convolution":[51],"(MHCMC)":[52],"module,":[53,73],"which":[54,74,130],"enhances":[55],"feature":[56,70],"by":[58],"expanding":[59],"field":[62],"utilizing":[64],"attention,":[66],"dynamic":[69,136],"aggregation":[71,137],"(DFA)":[72],"adaptively":[75],"prioritizes":[76],"crucial":[77],"spatial":[78],"features":[79],"based":[80],"on":[81],"information.":[83],"Additionally,":[84],"introduce":[86],"evolutionary":[88],"hybrid":[89],"network":[90],"(EHN)":[91],"simulate":[93],"transition":[95],"from":[96],"local":[97],"dependency":[100],"capture.":[101],"The":[102],"MHCMC":[103],"DFA":[105,114],"modules":[106,122],"are":[107,123],"first":[108],"fused":[109],"construct":[111],"(MHDFA)":[115],"module.":[116],"Subsequently,":[117],"MHDFA":[119],"EHN":[121],"sequentially":[124],"stacked":[125],"form":[127],"encoder,":[129],"denote":[132],"as":[133],"transformer":[138],"(MDAT).":[139],"By":[140],"further":[141],"integrating":[142],"MDAT":[143],"into":[144],"U\u2010Net":[146],"architecture,":[147],"obtain":[149],"proposed":[151],"MDAT\u2010Net.":[152],"Experimental":[153],"results":[154],"show":[155],"that":[156],"MDAT\u2010Net":[157],"outperforms":[158],"other":[159],"state\u2010of\u2010the\u2010art":[160],"models":[161],"three":[163],"tasks:":[167],"liver":[169],"tumor":[170],"benchmark":[172],"(LiTs2017),":[173],"CVC":[174],"LinicDB,":[175],"automated":[178],"cardiac":[179],"diagnosis":[180],"challenge":[181],"(ACDC).":[182]},"counts_by_year":[],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2026-01-14T00:00:00"}
