{"id":"https://openalex.org/W7161242151","doi":"https://doi.org/10.48550/arxiv.2605.14566","title":"SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation","display_name":"SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161242151","doi":"https://doi.org/10.48550/arxiv.2605.14566"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14566","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14566","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029072757","display_name":"Zhiquan Chen","orcid":"https://orcid.org/0000-0001-8382-9524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhiquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136194213","display_name":"Haitao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136223521","display_name":"Guowei Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Guowei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136194772","display_name":"Hejun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Hejun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.565500020980835,"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":0.565500020980835,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.12759999930858612,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.09229999780654907,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/segmentation","display_name":"Segmentation","score":0.6568999886512756},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5796999931335449},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5365999937057495},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4553999900817871},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4447000026702881},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.43549999594688416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4172999858856201},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3901999890804291},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3849000036716461}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6639000177383423},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6568999886512756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6541000008583069},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5796999931335449},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5365999937057495},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4553999900817871},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4447000026702881},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4171999990940094},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3901999890804291},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.33799999952316284},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C200873422","wikidata":"https://www.wikidata.org/wiki/Q5448821","display_name":"Filling-in","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.265500009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14566","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14566","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14566","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4702952802181244,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1],"segmentation":[2,38,79],"remains":[3],"challenging":[4],"in":[5,80,97,185],"low-data":[6,81,186],"regimes,":[7],"where":[8,106],"scarce":[9,127],"annotations":[10],"often":[11,30],"yield":[12],"poor":[13],"generalization":[14],"and":[15,42,48,94,159,174,188],"ambiguous":[16],"boundaries":[17],"with":[18,66,146,182],"missing":[19],"fine":[20],"structures.":[21],"Recent":[22],"self-supervised":[23],"pretraining":[24,65,119],"has":[25],"improved":[26,183],"transferability,":[27],"but":[28],"it":[29],"exhibits":[31],"a":[32,58,98,131,147],"texture":[33],"bias.":[34],"In":[35,69,139],"contrast,":[36],"accurate":[37],"is":[39],"inherently":[40],"geometry-aware":[41],"depends":[43],"on":[44,171],"both":[45],"topological":[46],"consistency":[47],"precise":[49],"boundary":[50,165,190],"preservation.":[51],"To":[52,83,121],"address":[53],"this":[54,84],"problem,":[55],"we":[56,71,86,129,141],"propose":[57,87],"two-stage":[59],"framework":[60],"that":[61,150],"couples":[62],"structure-aware":[63,75],"encoder":[64,145],"boundary-oriented":[67],"decoding.":[68],"Stage-1,":[70],"aim":[72],"to":[73,135],"learn":[74],"representations":[76],"for":[77,155,163],"downstream":[78],"regimes.":[82],"end,":[85],"Mixed-Domain":[88],"MeanFlow":[89],"Pretraining,":[90],"which":[91],"aligns":[92],"images":[93],"binary":[95],"masks":[96,107],"shared":[99],"latent":[100,103],"space":[101],"through":[102],"transport":[104],"regression,":[105],"act":[108],"as":[109],"conditional":[110],"structural":[111],"guidance":[112],"rather":[113],"than":[114],"prediction":[115],"targets,":[116],"making":[117],"the":[118,143],"task-agnostic.":[120],"further":[122],"improve":[123],"training":[124],"stability":[125],"under":[126,167],"supervision,":[128],"incorporate":[130],"lightweight":[132,148],"Dispersive":[133],"Loss":[134],"prevent":[136],"representation":[137],"collapse.":[138],"Stage-2,":[140],"fine-tune":[142],"pretrained":[144],"decoder":[149],"combines":[151],"Direct":[152],"Attentional":[153],"Fusion":[154],"adaptive":[156],"cross-scale":[157],"gating":[158],"Frequency-Directional":[160],"Dynamic":[161],"Convolution":[162],"high-frequency":[164],"refinement":[166],"appearance":[168],"variation.":[169],"Experiments":[170],"ISIC-2016,":[172],"Kvasir-SEG,":[173],"GlaS":[175],"demonstrate":[176],"consistent":[177],"gains":[178],"over":[179],"state-of-the-art":[180],"methods,":[181],"robustness":[184],"settings":[187],"sharper":[189],"delineation.":[191]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}
