{"id":"https://openalex.org/W4392904214","doi":"https://doi.org/10.1109/icassp48485.2024.10445821","title":"Weakly Semi-Supervised Tool Detection in Minimally Invasive Surgery Videos","display_name":"Weakly Semi-Supervised Tool Detection in Minimally Invasive Surgery Videos","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904214","doi":"https://doi.org/10.1109/icassp48485.2024.10445821"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10445821","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10445821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","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/A5076720835","display_name":"Ryo Fujii","orcid":"https://orcid.org/0000-0002-9115-8414"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryo Fujii","raw_affiliation_strings":["Keio University,Yokohama,Japan","Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020411666","display_name":"Ryo Hachiuma","orcid":"https://orcid.org/0000-0001-8274-3710"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Hachiuma","raw_affiliation_strings":["Keio University,Yokohama,Japan","NVIDIA, Taipei, Taiwan","Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"NVIDIA, Taipei, Taiwan","institution_ids":[]},{"raw_affiliation_string":"Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005819073","display_name":"Hideo Sait\u00f4","orcid":"https://orcid.org/0000-0002-2421-9862"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Saito","raw_affiliation_strings":["Keio University,Yokohama,Japan","Keio University, Yokohama, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Yokohama,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076720835"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":0.2501,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46349459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1611","last_page":"1615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9950000047683716,"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":0.9950000047683716,"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/T10916","display_name":"Surgical Simulation and Training","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9670000076293945,"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/computer-science","display_name":"Computer science","score":0.7773295640945435},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7668392658233643},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.7257670760154724},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6684754490852356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5941863656044006},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5303495526313782},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4788134694099426},{"id":"https://openalex.org/keywords/automatic-image-annotation","display_name":"Automatic image annotation","score":0.4669061303138733},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.42497238516807556},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4149479269981384},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.386574387550354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7773295640945435},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7668392658233643},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7257670760154724},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6684754490852356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5941863656044006},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5303495526313782},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4788134694099426},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.4669061303138733},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.42497238516807556},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4149479269981384},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.386574387550354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10445821","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10445821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320909","display_name":"Keio University","ror":"https://ror.org/02kn6nx58"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W611457968","https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W2160676519","https://openalex.org/W2519284461","https://openalex.org/W2565639579","https://openalex.org/W2587007321","https://openalex.org/W2592384071","https://openalex.org/W2807810534","https://openalex.org/W2813911573","https://openalex.org/W2963150697","https://openalex.org/W2963603913","https://openalex.org/W2963647178","https://openalex.org/W2970369250","https://openalex.org/W3003209274","https://openalex.org/W3003828048","https://openalex.org/W3021542222","https://openalex.org/W3096104140","https://openalex.org/W3130976481","https://openalex.org/W3178291178","https://openalex.org/W3204241019","https://openalex.org/W4311630744","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6684197973","https://openalex.org/W6733905022","https://openalex.org/W6739901393","https://openalex.org/W6766773940","https://openalex.org/W6773996751","https://openalex.org/W6776778719","https://openalex.org/W6789505266","https://openalex.org/W6841691558"],"related_works":["https://openalex.org/W2566406229","https://openalex.org/W3177930984","https://openalex.org/W2052697133","https://openalex.org/W2119028572","https://openalex.org/W2152482390","https://openalex.org/W2376984068","https://openalex.org/W2076896210","https://openalex.org/W2365617273","https://openalex.org/W2506386910","https://openalex.org/W2117928543"],"abstract_inverted_index":{"Surgical":[0],"tool":[1,48,100],"detection":[2,49,86],"is":[3,50],"essential":[4],"for":[5],"analyzing":[6],"and":[7,85,139],"evaluating":[8],"minimally":[9],"invasive":[10],"surgery":[11],"videos.":[12],"Current":[13],"approaches":[14],"are":[15,37,63],"mostly":[16],"based":[17],"on":[18,144],"supervised":[19],"methods":[20],"that":[21,98,128,133],"require":[22],"large,":[23],"fully":[24],"instance-level":[25,35,58,68],"labels":[26,36,55,59],"(i.e.,":[27],"bounding":[28],"boxes).":[29],"However,":[30],"large":[31],"image":[32,107],"datasets":[33],"with":[34],"often":[38,102],"limited":[39],"because":[40],"of":[41,44,57,115,155],"the":[42,80,113,118,124,131,145,153],"burden":[43,84],"annotation.":[45],"Thus,":[46],"surgical":[47],"important":[51],"when":[52],"providing":[53],"image-level":[54,61,110],"instead":[56],"since":[60],"annotations":[62],"considerably":[64],"more":[65],"time-efficient":[66],"than":[67],"annotations.":[69],"In":[70],"this":[71],"work,":[72],"we":[73],"propose":[74,90],"to":[75,108,122],"strike":[76],"a":[77,91,96],"balance":[78],"between":[79],"extremely":[81],"costly":[82],"annotation":[83],"performance.":[87],"We":[88],"further":[89],"co-occurrence":[92,116,119],"loss,":[93],"which":[94],"considers":[95],"characteristic":[97],"some":[99,134],"pairs":[101],"co-occur":[103],"together":[104],"in":[105,126,148],"an":[106],"leverage":[109],"labels.":[111],"Encapsulating":[112],"knowledge":[114],"using":[117],"loss":[120],"helps":[121],"overcome":[123],"difficulty":[125],"classification":[127],"originates":[129],"from":[130],"fact":[132],"tools":[135],"have":[136],"similar":[137],"shapes":[138],"textures.":[140],"Extensive":[141],"experiments":[142],"conducted":[143],"Endovis2018":[146],"dataset":[147],"various":[149],"data":[150],"settings":[151],"show":[152],"effectiveness":[154],"our":[156],"method.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}