{"id":"https://openalex.org/W7148617457","doi":"https://doi.org/10.48550/arxiv.2604.00684","title":"TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation","display_name":"TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148617457","doi":"https://doi.org/10.48550/arxiv.2604.00684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.00684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132827354","display_name":"Jiawei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132827153","display_name":"Qiangqiang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Qiangqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132815348","display_name":"Dandan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Dandan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132822428","display_name":"Yong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132822014","display_name":"Yugen Yi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi, Yugen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059718822","display_name":"Xiaoqi Zhao","orcid":"https://orcid.org/0000-0001-7734-5128"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xiaoqi","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":0.46549999713897705,"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":0.46549999713897705,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.15189999341964722,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.09969999641180038,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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":0.7659000158309937},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5698000192642212},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5055000185966492},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4805999994277954},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45170000195503235},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44530001282691956},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4377000033855438},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4221999943256378}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7659000158309937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398999929428101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6541000008583069},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5698000192642212},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45170000195503235},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3734999895095825},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2621000111103058},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5196360349655151,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"},{"score":0.42030951380729675,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Building":[0],"a":[1,5,21,59,80,113],"unified":[2,28,63,136],"model":[3],"with":[4],"single":[6],"set":[7],"of":[8,15,120],"parameters":[9],"to":[10,44,116],"efficiently":[11],"handle":[12],"diverse":[13,89],"types":[14],"medical":[16,64,90,142],"lesion":[17,51,65,94,143],"segmentation":[18,29,137,144],"has":[19],"become":[20],"crucial":[22],"objective":[23],"for":[24,62],"AI-assisted":[25],"diagnosis.":[26],"Existing":[27],"approaches":[30],"typically":[31],"rely":[32],"on":[33],"shared":[34,75],"encoders":[35],"across":[36,88,139],"heterogeneous":[37],"tasks":[38,145],"and":[39,49,76,93,111,123,128,135,154],"modalities,":[40,149],"which":[41],"often":[42],"leads":[43],"feature":[45,86],"entanglement,":[46],"gradient":[47],"interference,":[48],"suboptimal":[50],"discrimination.":[52],"In":[53],"this":[54],"work,":[55],"we":[56],"propose":[57],"TP-Seg,":[58],"task-prototype":[60],"framework":[61],"segmentation.":[66],"On":[67,96],"one":[68],"hand,":[69,99],"the":[70,97,100],"task-conditioned":[71],"adapter":[72],"effectively":[73],"balances":[74],"task-specific":[77,121],"representations":[78],"through":[79],"dual-path":[81],"expert":[82],"structure,":[83],"enabling":[84],"adaptive":[85],"extraction":[87],"imaging":[91,148],"modalities":[92],"types.":[95],"other":[98],"prototype-guided":[101],"task":[102,106],"decoder":[103],"introduces":[104],"learnable":[105],"prototypes":[107],"as":[108],"semantic":[109],"anchors":[110],"employs":[112],"cross-attention":[114],"mechanism":[115],"achieve":[117],"fine-grained":[118],"modeling":[119],"foreground":[122],"background":[124],"semantics.":[125],"Without":[126],"bells":[127],"whistles,":[129],"TP-Seg":[130],"consistently":[131],"outperforms":[132],"specialized,":[133],"general":[134],"methods":[138],"8":[140],"different":[141],"covering":[146],"multiple":[147],"demonstrating":[150],"strong":[151],"generalization,":[152],"scalability":[153],"clinical":[155],"applicability.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}
