{"id":"https://openalex.org/W7161233822","doi":"https://doi.org/10.48550/arxiv.2605.14579","title":"Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation","display_name":"Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161233822","doi":"https://doi.org/10.48550/arxiv.2605.14579"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14579","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14579","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.2605.14579","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":0.5162000060081482,"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":0.5162000060081482,"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":0.0851999968290329,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.07280000299215317,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6662999987602234},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5358999967575073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5174000263214111},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49540001153945923},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48089998960494995},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41679999232292175},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40540000796318054}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.718999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7111999988555908},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6662999987602234},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6001999974250793},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5358999967575073},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5339000225067139},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48089998960494995},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3549000024795532},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.3458999991416931},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2766000032424927},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14579","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14579","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.2605.14579","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14579","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"medical":[1,68],"image":[2,69],"segmentation":[3,70],"is":[4],"fundamental":[5],"to":[6,35,45,140],"precision":[7],"medicine,":[8],"yet":[9],"robust":[10],"delineation":[11],"remains":[12],"challenging":[13],"under":[14],"heterogeneous":[15],"appearances,":[16],"ambiguous":[17],"boundaries,":[18],"and":[19,25,30,38,50,77,114,145,179],"large":[20],"anatomical":[21,78],"variability.":[22],"Similar":[23],"intensity":[24],"texture":[26,144],"patterns":[27],"between":[28],"targets":[29],"surrounding":[31],"tissues":[32],"often":[33],"lead":[34],"blurred":[36],"activations":[37],"unreliable":[39],"separation.":[40],"We":[41],"attribute":[42],"these":[43,59,123],"failures":[44],"representation":[46,75],"collapse":[47],"during":[48],"encoding":[49],"insufficient":[51],"fine":[52,91],"grained":[53,92],"multi":[54,137,172],"scale":[55,138],"decoding.":[56],"To":[57],"address":[58],"issues,":[60],"we":[61],"propose":[62],"Med":[63,80,166],"DisSeg,":[64],"a":[65,83],"dispersion":[66],"driven":[67],"framework":[71],"that":[72],"jointly":[73],"improves":[74],"learning":[76],"delineation.":[79],"DisSeg":[81,167],"combines":[82],"lightweight":[84],"Dispersive":[85,96],"Loss":[86,97],"with":[87,118],"adaptive":[88,136],"attention":[89],"for":[90],"structure":[93,129],"segmentation.":[94],"The":[95],"enlarges":[98],"inter":[99],"sample":[100],"margins":[101],"by":[102],"treating":[103],"in":[104],"batch":[105],"hidden":[106],"representations":[107],"as":[108],"negative":[109],"pairs,":[110],"producing":[111],"well":[112],"dispersed":[113],"boundary":[115],"aware":[116],"embeddings":[117],"negligible":[119],"overhead.":[120],"Based":[121],"on":[122,151,171],"enhanced":[124],"representations,":[125],"the":[126,133,162],"encoder":[127],"strengthens":[128],"sensitive":[130],"responses,":[131],"while":[132],"decoder":[134],"performs":[135],"calibration":[139],"preserve":[141],"complementary":[142],"local":[143],"global":[146],"shape":[147],"information.":[148],"Extensive":[149],"experiments":[150],"five":[152],"datasets":[153],"spanning":[154],"three":[155],"imaging":[156],"modalities":[157],"demonstrate":[158],"consistent":[159],"state":[160],"of":[161],"art":[163],"performance.":[164],"Moreover,":[165],"achieves":[168],"competitive":[169],"results":[170],"organ":[173],"CT":[174],"segmentation,":[175],"supporting":[176],"its":[177],"robustness":[178],"cross":[180],"task":[181],"applicability.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}