{"id":"https://openalex.org/W4414360682","doi":"https://doi.org/10.24963/ijcai.2025/139","title":"Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation","display_name":"Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360682","doi":"https://doi.org/10.24963/ijcai.2025/139"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/139","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jianchao Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianchao Jiang","raw_affiliation_strings":["Nanjing University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064008061","display_name":"Haofeng Zhang","orcid":"https://orcid.org/0000-0002-4039-7618"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haofeng Zhang","raw_affiliation_strings":["Nanjing University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1242","last_page":"1250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.666100025177002,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.666100025177002,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.5954999923706055,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.5821999907493591,"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":0.6949999928474426},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6444000005722046},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.609499990940094},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.532800018787384},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5214999914169312},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.46880000829696655},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.45890000462532043},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.453900009393692},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4440999925136566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7332000136375427},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6949999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6948000192642212},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6444000005722046},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.609499990940094},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5444999933242798},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.532800018787384},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5214999914169312},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.46880000829696655},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.453900009393692},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4205000102519989},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.387800008058548},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3131999969482422},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.2870999872684479},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.27959999442100525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/139","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"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":{"Few-Shot":[0],"Medical":[1],"Image":[2],"Segmentation":[3],"(FSMIS)":[4],"has":[5],"been":[6],"widely":[7],"used":[8],"to":[9,51,70,74,93,119,139,148,165],"train":[10],"a":[11,19,64,88,111,130,145,162],"model":[12],"that":[13,78,182],"can":[14,44],"perform":[15],"segmentation":[16,83],"from":[17,32],"only":[18],"few":[20],"annotated":[21],"images.":[22,158],"However,":[23],"most":[24],"existing":[25],"prototype-based":[26],"FSMIS":[27],"methods":[28],"generate":[29,120,140],"multiple":[30,121],"prototypes":[31,123],"the":[33,52,59,150,168],"support":[34,101,108],"image":[35,179],"solely":[36],"by":[37,98,106],"random":[38],"sampling":[39],"or":[40],"local":[41],"averaging,":[42],"which":[43],"cause":[45],"particularly":[46],"severe":[47],"boundary":[48,163],"blurring":[49],"due":[50],"tendency":[53],"for":[54,58,81],"normal":[55],"features":[56,62,77,97],"accounting":[57],"majority":[60],"of":[61,63,170],"specific":[65],"category.":[66],"Consequently,":[67],"we":[68,86,160],"propose":[69],"focus":[71],"more":[72],"attention":[73],"those":[75],"weaker":[76],"are":[79],"crucial":[80],"clear":[82],"boundary.":[84],"Specifically,":[85],"design":[87],"Support":[89],"Self-Prediction":[90],"(SSP)":[91],"module":[92,116,136],"identify":[94],"such":[95],"weak":[96,127],"comparing":[99],"true":[100],"mask":[102,143],"with":[103],"one":[104],"predicted":[105],"global":[107],"prototype.":[109],"Then,":[110],"Hard":[112],"Prototypes":[113],"Generation":[114],"(HPG)":[115],"is":[117,137,189],"employed":[118],"hard":[122],"based":[124],"on":[125,174],"these":[126],"features.":[128],"Subsequently,":[129],"Multiple":[131],"Similarity":[132],"Maps":[133],"Fusion":[134],"(MSMF)":[135],"devised":[138],"final":[141],"segmenting":[142],"in":[144,156],"dual-path":[146],"fashion":[147],"mitigate":[149],"imbalance":[151],"between":[152],"foreground":[153],"and":[154],"background":[155],"medical":[157,178],"Furthermore,":[159],"introduce":[161],"loss":[164],"further":[166],"constraint":[167],"edge":[169],"segmentation.":[171],"Extensive":[172],"experiments":[173],"three":[175],"publicly":[176],"available":[177,190],"datasets":[180],"demonstrate":[181],"our":[183],"method":[184],"achieves":[185],"state-of-the-art":[186],"performance.":[187],"Code":[188],"at":[191],"https://github.com/jcjiang99/CoW.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
