{"id":"https://openalex.org/W4402915444","doi":"https://doi.org/10.1109/icip51287.2024.10647414","title":"Advancing Colorectal Polyp Segmentation With Watershed Algorithm-Enhanced Parallel Self-Supervised Learning","display_name":"Advancing Colorectal Polyp Segmentation With Watershed Algorithm-Enhanced Parallel Self-Supervised Learning","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402915444","doi":"https://doi.org/10.1109/icip51287.2024.10647414"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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":"https://openalex.org/A5017831967","display_name":"Khalil Chikhaoui","orcid":"https://orcid.org/0000-0002-2406-5752"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Khalil Chikhaoui","raw_affiliation_strings":["King Fahd University of Petroleum and Minerals,Electrical Engineering Department,Dhahran,Saudi Arabia,31261"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Fahd University of Petroleum and Minerals,Electrical Engineering Department,Dhahran,Saudi Arabia,31261","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063764685","display_name":"Motaz Alfarraj","orcid":"https://orcid.org/0000-0002-6052-7221"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Motaz Alfarraj","raw_affiliation_strings":["King Fahd University of Petroleum and Minerals,Electrical Engineering Department,Dhahran,Saudi Arabia,31261"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Fahd University of Petroleum and Minerals,Electrical Engineering Department,Dhahran,Saudi Arabia,31261","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":null,"apc_paid":null,"fwci":1.7734,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84210526,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3124","last_page":"3130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.8205000162124634,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.8205000162124634,"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/T10696","display_name":"Gastric Cancer Management and Outcomes","score":0.7774999737739563,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.7477999925613403,"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/watershed","display_name":"Watershed","score":0.7555642127990723},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6816783547401428},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5937045216560364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48406997323036194},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4694361686706543},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3738512694835663},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3465529680252075},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28628772497177124}],"concepts":[{"id":"https://openalex.org/C150547873","wikidata":"https://www.wikidata.org/wiki/Q947851","display_name":"Watershed","level":2,"score":0.7555642127990723},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6816783547401428},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5937045216560364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48406997323036194},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4694361686706543},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3738512694835663},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3465529680252075},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28628772497177124}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip51287.2024.10647414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322323","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W151895561","https://openalex.org/W1901129140","https://openalex.org/W2107949840","https://openalex.org/W2129494114","https://openalex.org/W2134158406","https://openalex.org/W2163214467","https://openalex.org/W2883190781","https://openalex.org/W2908140063","https://openalex.org/W2922490216","https://openalex.org/W2997286550","https://openalex.org/W2999580839","https://openalex.org/W3092344722","https://openalex.org/W3120430728","https://openalex.org/W4211219842","https://openalex.org/W4221130522","https://openalex.org/W4251431888","https://openalex.org/W4312603774","https://openalex.org/W4312804044","https://openalex.org/W4313156423","https://openalex.org/W4318213581","https://openalex.org/W4367055910","https://openalex.org/W6779977557","https://openalex.org/W6796761347"],"related_works":["https://openalex.org/W1992288292","https://openalex.org/W2493062119","https://openalex.org/W1980614686","https://openalex.org/W2051487156","https://openalex.org/W4387955966","https://openalex.org/W2144571534","https://openalex.org/W1134571055","https://openalex.org/W2343332128","https://openalex.org/W214080024","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Colorectal":[0],"cancer":[1],"(CRC)":[2],"is":[3],"a":[4,85],"major":[5],"health":[6],"concern":[7],"globally,":[8],"known":[9],"for":[10,39,74],"its":[11],"high":[12],"prevalence":[13],"and":[14,32,42,59,98,123],"mortality":[15],"rate.":[16],"It":[17],"typically":[18],"arises":[19],"from":[20],"precancerous":[21],"growths":[22],"called":[23],"polyps":[24,36],"within":[25],"the":[26,108],"colon":[27],"or":[28],"rectum.":[29],"Early":[30],"detection":[31],"removal":[33],"of":[34,53],"these":[35],"are":[37],"crucial":[38],"preventing":[40],"CRC":[41],"improving":[43],"patient":[44],"outcomes.":[45],"Traditional":[46],"deep":[47],"learning":[48,71],"models":[49],"require":[50],"large":[51],"amounts":[52],"labeled":[54],"data,":[55],"making":[56],"them":[57],"costly":[58],"time-consuming":[60],"to":[61,100],"develop.":[62],"To":[63],"address":[64],"this,":[65],"we":[66],"present":[67],"an":[68],"innovative":[69],"self-supervised":[70],"(SSL)":[72],"approach":[73,91],"colorectal":[75],"polyp":[76],"segmentation,":[77],"which":[78],"integrates":[79],"watershed":[80],"algorithm-enhanced":[81],"pseudo-label":[82],"generation":[83],"with":[84],"novel":[86],"parallel":[87],"pretraining":[88],"method.":[89],"This":[90],"leverages":[92],"dual-task":[93],"pre-training":[94],"on":[95,107],"image":[96,134],"reconstruction":[97],"pseudo-segmentation":[99],"extract":[101],"more":[102],"relevant":[103],"feature":[104],"representations.":[105],"Validated":[106],"Kvasir-SEG":[109],"dataset":[110],"using":[111],"three":[112],"different":[113],"models,":[114],"our":[115],"methodology":[116],"demonstrates":[117],"significant":[118],"improvements":[119],"in":[120,126,132],"segmentation":[121],"accuracy":[122],"efficiency,":[124],"particularly":[125],"low-data":[127],"scenarios,":[128],"suggesting":[129],"broader":[130],"applicability":[131],"medical":[133],"analysis.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}