{"id":"https://openalex.org/W7134844212","doi":"https://doi.org/10.48550/arxiv.2603.08605","title":"Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation","display_name":"Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134844212","doi":"https://doi.org/10.48550/arxiv.2603.08605"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.08605","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128668275","display_name":"Hikmat Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Khan, Hikmat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128639327","display_name":"Wei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128667227","display_name":"Muhammad Khalid Khan Niazi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niazi, Muhammad Khalid Khan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5128668275"],"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/T10862","display_name":"AI in cancer detection","score":0.9362000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9362000226974487,"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"}},{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.026399999856948853,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.009399999864399433,"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/segmentation","display_name":"Segmentation","score":0.788100004196167},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7817999720573425},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5511000156402588},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5156999826431274},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.40720000863075256},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39579999446868896},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.788100004196167},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7817999720573425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6844000220298767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6607000231742859},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29899999499320984},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.08605","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.08605","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08605","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.08605","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6222428679466248}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background":[0],"and":[1,29,61,81,110,142,148,161,179,195,217],"objectives:":[2],"Colorectal":[3],"cancer":[4,128],"histopathological":[5],"grading":[6],"depends":[7],"on":[8,19,124,149,192,204],"accurate":[9],"segmentation":[10,40,222],"of":[11,103,139,177,184],"glandular":[12,66,118],"structures.":[13,67],"Current":[14],"deep":[15],"learning":[16],"approaches":[17],"rely":[18],"large":[20],"scale":[21],"pixel":[22],"level":[23],"annotations":[24,80],"that":[25,56,76],"are":[26],"labor":[27],"intensive":[28],"difficult":[30],"to":[31,63,89,114],"obtain":[32],"in":[33,223],"routine":[34],"clinical":[35],"practice.":[36],"Weakly":[37],"supervised":[38,72],"semantic":[39],"offers":[41],"a":[42,70,172,180],"promising":[43],"alternative.":[44],"However,":[45],"class":[46],"activation":[47],"map":[48],"based":[49,99],"methods":[50],"often":[51],"produce":[52],"incomplete":[53],"pseudo":[54,92],"masks":[55],"emphasize":[57],"highly":[58],"discriminative":[59],"regions":[60],"fail":[62],"supervise":[64],"unannotated":[65,117],"We":[68],"propose":[69],"weakly":[71],"teacher":[73,87,104],"student":[74],"framework":[75,96,170,212],"leverages":[77],"sparse":[78],"pathologist":[79],"an":[82,125,214],"Exponential":[83],"Moving":[84],"Average":[85],"stabilized":[86],"network":[88],"generate":[90],"refined":[91],"masks.":[93],"Methods:":[94],"The":[95,120,131,210],"integrates":[97],"confidence":[98],"filtering,":[100],"adaptive":[101],"fusion":[102],"predictions":[105],"with":[106],"limited":[107],"ground":[108],"truth,":[109],"curriculum":[111],"guided":[112],"refinement":[113],"progressively":[115],"segment":[116],"regions.":[119],"method":[121],"was":[122],"evaluated":[123],"institutional":[126],"colorectal":[127,224],"cohort":[129,187],"from":[130],"Ohio":[132],"State":[133],"University":[134],"Wexner":[135],"Medical":[136],"Center":[137],"consisting":[138],"60":[140],"hematoxylin":[141],"eosin":[143],"stained":[144],"whole":[145],"slide":[146],"images":[147],"public":[150],"datasets":[151],"including":[152],"the":[153,165,169],"Gland":[154,166],"Segmentation":[155,167],"dataset,":[156],"TCGA":[157,159,193,196],"COAD,":[158],"READ,":[160],"SPIDER.":[162],"Results:":[163],"On":[164],"dataset":[168],"achieved":[171],"mean":[173,181],"Intersection":[174],"over":[175],"Union":[176],"80.10":[178],"Dice":[182],"coefficient":[183],"89.10.":[185],"Cross":[186],"evaluation":[188],"demonstrated":[189],"robust":[190],"generalization":[191],"COAD":[194],"READ":[197],"without":[198],"additional":[199],"annotations,":[200],"while":[201],"reduced":[202],"performance":[203],"SPIDER":[205],"reflected":[206],"domain":[207],"shift.":[208],"Conclusions:":[209],"proposed":[211],"provides":[213],"annotation":[215],"efficient":[216],"generalizable":[218],"approach":[219],"for":[220],"gland":[221],"histopathology.":[225]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-11T00:00:00"}
