{"id":"https://openalex.org/W7164878873","doi":"https://doi.org/10.1016/j.inffus.2026.104551","title":"An efficient frequency-aware network with local-global dynamics fusion for memory-constrained clinical medical image classification","display_name":"An efficient frequency-aware network with local-global dynamics fusion for memory-constrained clinical medical image classification","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7164878873","doi":"https://doi.org/10.1016/j.inffus.2026.104551"},"language":"en","primary_location":{"id":"doi:10.1016/j.inffus.2026.104551","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.inffus.2026.104551","pdf_url":null,"source":{"id":"https://openalex.org/S7560371","display_name":"Information Fusion","issn_l":"1566-2535","issn":["1566-2535","1872-6305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Fusion","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.inffus.2026.104551","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110644850","display_name":"K. Yang","orcid":"https://orcid.org/0009-0001-7170-7586"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Kaiwen Yang","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China"],"raw_orcid":"https://orcid.org/0009-0001-7170-7586","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044017450","display_name":"Yaofei Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Yaofei Duan","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China"],"raw_orcid":"https://orcid.org/0009-0007-2562-5794","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113355158","display_name":"Nuoer Long","orcid":"https://orcid.org/0009-0003-8719-7722"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Nuoer Long","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100833972","display_name":"Yue Sun","orcid":"https://orcid.org/0009-0004-3908-7198"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Yue Sun","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046605531","display_name":"Jungong Han","orcid":"https://orcid.org/0000-0003-4361-956X"},"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":"Jungong Han","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101628585","display_name":"Tao Tan","orcid":"https://orcid.org/0000-0001-5403-0887"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Tao Tan","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, 999078, China","institution_ids":["https://openalex.org/I49835588"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101628585"],"corresponding_institution_ids":["https://openalex.org/I49835588"],"apc_list":{"value":4360,"currency":"USD","value_usd":4360},"apc_paid":{"value":4360,"currency":"USD","value_usd":4360},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72531552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"136","issue":null,"first_page":"104551","last_page":"104551"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.4424999952316284,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.4424999952316284,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.2328999936580658,"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/T11019","display_name":"Image Enhancement Techniques","score":0.09730000048875809,"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/kernel","display_name":"Kernel (algebra)","score":0.5565999746322632},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48910000920295715},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.48739999532699585},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4602999985218048},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.43380001187324524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41269999742507935},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.41130000352859497},{"id":"https://openalex.org/keywords/fusion-rules","display_name":"Fusion rules","score":0.4016999900341034},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.3903000056743622},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3779999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7853000164031982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5705999732017517},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5565999746322632},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48910000920295715},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4602999985218048},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C2778971668","wikidata":"https://www.wikidata.org/wiki/Q5510284","display_name":"Fusion rules","level":4,"score":0.4016999900341034},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3601999878883362},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34279999136924744},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.32839998602867126},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.3025999963283539},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2538999915122986},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.inffus.2026.104551","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.inffus.2026.104551","pdf_url":null,"source":{"id":"https://openalex.org/S7560371","display_name":"Information Fusion","issn_l":"1566-2535","issn":["1566-2535","1872-6305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Fusion","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.inffus.2026.104551","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.inffus.2026.104551","pdf_url":null,"source":{"id":"https://openalex.org/S7560371","display_name":"Information Fusion","issn_l":"1566-2535","issn":["1566-2535","1872-6305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Fusion","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2760880057","https://openalex.org/W2895720320","https://openalex.org/W2916845318","https://openalex.org/W3004531689","https://openalex.org/W3025011581","https://openalex.org/W3164581645","https://openalex.org/W3166842197","https://openalex.org/W3168997536","https://openalex.org/W4322766760","https://openalex.org/W4324144441","https://openalex.org/W4390588537","https://openalex.org/W4393053253","https://openalex.org/W4397032023","https://openalex.org/W4398243947","https://openalex.org/W4401102117","https://openalex.org/W4415707663"],"related_works":[],"abstract_inverted_index":{"Convolutional":[0,181],"networks,":[1],"Transformers,":[2],"Mamba-based":[3],"state":[4],"space":[5],"models,":[6],"and":[7,33,73,98,138,160,191,198,273,298,315],"their":[8],"hybrid":[9,71],"variants":[10],"have":[11,154],"shown":[12],"promise":[13],"in":[14,36,53,101],"medical":[15,56,152,207,295],"image":[16,196],"classification,":[17],"yet":[18],"they":[19],"struggle":[20],"with":[21,134,167,222],"real-world":[22],"clinical":[23,323],"challenges":[24],"such":[25],"as":[26,271],"heterogeneous":[27],"imaging":[28,296],"quality,":[29],"texture-rich":[30],"anatomical":[31],"structures,":[32],"edge":[34,97,115],"ambiguity":[35],"low-resolution":[37],"features.":[38,244],"To":[39],"address":[40],"these":[41],"challenges,":[42],"including":[43],"the":[44,158,202,299],"bottleneck":[45],"of":[46,157,162,171],"Graphics":[47],"Processing":[48],"Unit":[49],"(GPU)":[50],"memory":[51,263,279],"consumption":[52,280],"processing":[54,133],"high-resolution":[55],"images,":[57],"we":[58,175],"propose":[59],"MFGENet":[60,252,275],"(Multi-scale":[61],"Fusion":[62,183,212],"Global":[63,119],"Enhancement":[64],"Network),":[65],"a":[66,79,126,178,210],"novel":[67],"architecture":[68],"integrating":[69],"frequency-spatial":[70],"representation":[72],"efficient":[74],"local-global":[75,312],"context":[76,313],"modeling.":[77],"First,":[78],"wavelet-based":[80],"stem":[81],"module":[82],"replaces":[83],"conventional":[84],"downsampling,":[85],"decomposing":[86],"features":[87,156],"via":[88],"Haar":[89],"transform":[90],"into":[91],"multi-frequency":[92],"components.":[93],"This":[94],"preserves":[95],"critical":[96],"texture":[99],"details":[100],"high-frequency":[102],"maps":[103],"while":[104,146,241,257,285],"using":[105],"low-frequency":[106],"semantics":[107],"to":[108,142,194,205,234,246,283],"generate":[109],"adaptive":[110],"gating":[111],"controls,":[112],"significantly":[113,254,276],"mitigating":[114],"blurring.":[116],"Second,":[117],"our":[118],"Dynamic":[120,180],"Enhanced":[121],"Block":[122],"(GDE":[123],"Block)":[124],"incorporates":[125],"parallel":[127,135],"enhancement":[128],"subnetwork,":[129],"which":[130],"employs":[131],"group-wise":[132],"dilated":[136],"convolution":[137,188],"spatial-channel":[139],"attention":[140],"paths":[141],"capture":[143,195],"long-range":[144],"dependencies":[145],"maintaining":[147,258,286],"computational":[148],"efficiency.":[149],"Since":[150],"different":[151,206],"images":[153],"varying":[155],"lesion":[159,173],"Region":[161],"Interest":[163],"(ROI)":[164],"focus":[165],"scales,":[166],"even":[168],"differing":[169],"numbers":[170],"ROI":[172],"features,":[174,200],"also":[176],"designed":[177],"Multi-Path":[179],"Residual":[182],"(MPDConv)":[184],"that":[185,302],"dynamically":[186],"adjusts":[187],"layer":[189],"counts":[190],"kernel":[192],"sizes":[193],"diversity":[197],"multi-scale":[199],"enhancing":[201],"network\u2019s":[203],"adaptability":[204],"images.":[208],"Third,":[209],"Multi-scale":[211],"Attention":[213],"Module":[214],"(MFA":[215],"Module)":[216],"introduces":[217],"an":[218,306],"additive":[219],"similarity":[220],"function":[221],"multi-kernel":[223],"depthwise":[224],"convolutions,":[225],"reducing":[226],"quadratic":[227],"complexity":[228,236],"O":[229,237],"(":[230,238],"N":[231,239],"2":[232],")":[233,240],"linear":[235],"fusing":[242],"cross-scale":[243],"Compared":[245],"lightweight":[247],"models":[248,269],"(e.g.,":[249],"EfficientNet-B3,":[250],"ConvNeXt-T),":[251],"achieves":[253],"higher":[255],"accuracy":[256],"comparable":[259],"or":[260],"lower":[261],"GPU":[262,278],"consumption.":[264],"When":[265],"evaluated":[266],"against":[267],"high-accuracy":[268],"(such":[270],"MedViT":[272],"MedMamba),":[274],"reduces":[277],"by":[281],"up":[282],"62%":[284],"identical":[287],"performance":[288],"levels.":[289],"We":[290],"conducted":[291],"comparisons":[292],"across":[293],"16":[294],"datasets,":[297],"results":[300],"demonstrate":[301],"MFGENet\u2019s":[303],"design":[304],"enables":[305],"effective":[307],"balance":[308],"among":[309],"structural":[310],"sensitivity,":[311],"modeling,":[314],"resource":[316],"efficiency,":[317],"making":[318],"it":[319],"well-suited":[320],"for":[321],"memory-constrained":[322],"applications.":[324]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-06-17T00:00:00"}
