{"id":"https://openalex.org/W4224981972","doi":"https://doi.org/10.1109/isbi52829.2022.9761661","title":"Attention-Based Deep Multiple Instance Learning with Adaptive Instance Sampling","display_name":"Attention-Based Deep Multiple Instance Learning with Adaptive Instance Sampling","publication_year":2022,"publication_date":"2022-03-28","ids":{"openalex":"https://openalex.org/W4224981972","doi":"https://doi.org/10.1109/isbi52829.2022.9761661"},"language":"en","primary_location":{"id":"doi:10.1109/isbi52829.2022.9761661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761661","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","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/A5011873035","display_name":"Aliasghar Tarkhan","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aliasghar Tarkhan","raw_affiliation_strings":["University of Washington,Department of Biostatistics,Seattle,WA","Department of Biostatistics, University of Washington, Seattle, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Department of Biostatistics,Seattle,WA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Department of Biostatistics, University of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Trung Kien Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trung Kien Nguyen","raw_affiliation_strings":["Genentech,PHC Imaging Group,South San Francisco,CA","PHC Imaging Group, Genentech, South San Francisco, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Genentech,PHC Imaging Group,South San Francisco,CA","institution_ids":[]},{"raw_affiliation_string":"PHC Imaging Group, Genentech, South San Francisco, CA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087655518","display_name":"Noah Simon","orcid":"https://orcid.org/0000-0002-8985-2474"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noah Simon","raw_affiliation_strings":["University of Washington,Department of Biostatistics,Seattle,WA","Department of Biostatistics, University of Washington, Seattle, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington,Department of Biostatistics,Seattle,WA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Department of Biostatistics, University of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091515666","display_name":"Thomas Bengtsson","orcid":"https://orcid.org/0000-0002-9667-8873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Bengtsson","raw_affiliation_strings":["Genentech,PHC Imaging Group,South San Francisco,CA","PHC Imaging Group, Genentech, South San Francisco, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Genentech,PHC Imaging Group,South San Francisco,CA","institution_ids":[]},{"raw_affiliation_string":"PHC Imaging Group, Genentech, South San Francisco, CA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003913749","display_name":"Paolo Ocampo","orcid":"https://orcid.org/0000-0003-4876-0861"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paolo Ocampo","raw_affiliation_strings":["Genentech,PHC Imaging Group,South San Francisco,CA","PHC Imaging Group, Genentech, South San Francisco, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Genentech,PHC Imaging Group,South San Francisco,CA","institution_ids":[]},{"raw_affiliation_string":"PHC Imaging Group, Genentech, South San Francisco, CA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040440410","display_name":"Jian S. Dai","orcid":"https://orcid.org/0000-0002-9729-1662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Dai","raw_affiliation_strings":["Genentech,PHC Imaging Group,South San Francisco,CA","PHC Imaging Group, Genentech, South San Francisco, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Genentech,PHC Imaging Group,South San Francisco,CA","institution_ids":[]},{"raw_affiliation_string":"PHC Imaging Group, Genentech, South San Francisco, CA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.462,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70835038,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9889000058174133,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9889000058174133,"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/T10862","display_name":"AI in cancer detection","score":0.9812999963760376,"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":0.9757000207901001,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8389010429382324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7852128744125366},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7208175659179688},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6272448897361755},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6175772547721863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5748488903045654},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5714767575263977},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5525010228157043},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5429072976112366},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5326827168464661},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.5026946067810059},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47028377652168274},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.46810197830200195},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4208950102329254},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3389896750450134},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.09418711066246033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8389010429382324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7852128744125366},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7208175659179688},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6272448897361755},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6175772547721863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5748488903045654},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5714767575263977},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5525010228157043},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5429072976112366},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5326827168464661},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.5026946067810059},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47028377652168274},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.46810197830200195},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4208950102329254},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3389896750450134},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.09418711066246033},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi52829.2022.9761661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761661","pdf_url":null,"source":{"id":"https://openalex.org/S4363605129","display_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332193","display_name":"Genentech","ror":"https://ror.org/04gndp242"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W24402856","https://openalex.org/W1582774210","https://openalex.org/W1998118468","https://openalex.org/W2096451472","https://openalex.org/W2115672776","https://openalex.org/W2135607475","https://openalex.org/W2165544861","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2568799344","https://openalex.org/W2571156573","https://openalex.org/W2780025726","https://openalex.org/W2785934082","https://openalex.org/W3007600959","https://openalex.org/W3018295606","https://openalex.org/W3043535018","https://openalex.org/W3113084721","https://openalex.org/W3121943121","https://openalex.org/W3135547872","https://openalex.org/W3162420793","https://openalex.org/W3216903807","https://openalex.org/W4234583614","https://openalex.org/W4288089649","https://openalex.org/W6600987510","https://openalex.org/W6687483927","https://openalex.org/W6747701563","https://openalex.org/W6769428822","https://openalex.org/W6774299990","https://openalex.org/W6787247776","https://openalex.org/W6804744604"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W1533177136","https://openalex.org/W4380994516","https://openalex.org/W2556260348"],"abstract_inverted_index":{"One":[0],"challenge":[1],"of":[2,18,69,79,93,117],"training":[3,72],"deep":[4,74,104],"neural":[5,75],"networks":[6],"with":[7,77,123],"gigapixel":[8],"whole-slide":[9],"images":[10],"(WSIs)":[11],"in":[12],"computational":[13],"pathology":[14],"is":[15,85,142],"the":[16,28,58,67,136,145],"lack":[17],"annotation":[19],"at":[20],"pixel":[21,61],"level":[22,25],"or":[23,62,125],"regional":[24],"due":[26],"to":[27,52],"high":[29],"cost":[30,68],"and":[31,39,88,119],"time-consuming":[32],"labeling":[33],"effort.":[34],"Multiple":[35],"instance":[36],"learning":[37,47],"(MIL)":[38],"its":[40],"attention-based":[41,103,147],"versions":[42],"are":[43,114],"typical":[44],"weakly":[45],"supervised":[46],"methods,":[48],"which":[49],"allow":[50],"us":[51],"use":[53],"slide-level":[54],"labels":[55],"directly,":[56],"without":[57],"need":[59],"for":[60,95,155],"region":[63],"labels,":[64],"thus":[65],"reducing":[66],"annotation.":[70],"However,":[71],"a":[73,91,100],"network":[76],"thousands":[78],"image":[80,111,121,153],"regions":[81,112,122,154],"(patches)":[82],"per":[83],"slide":[84],"computationally":[86],"expensive,":[87],"it":[89,141],"needs":[90],"lot":[92],"time":[94],"convergence.":[96],"This":[97,107],"paper":[98],"proposes":[99],"fast":[101],"adaptive":[102],"MIL":[105,148],"approach.":[106],"approach":[108,134,139],"adaptively":[109],"selects":[110],"that":[113,131],"highly":[115],"predictive":[116],"outcome":[118],"ignores":[120],"little":[124],"no":[126],"information.":[127],"We":[128],"empirically":[129],"show":[130],"our":[132],"proposed":[133],"outperforms":[135],"random":[137],"sampling":[138],"while":[140],"faster":[143],"than":[144],"standard":[146],"method":[149],"(which":[150],"uses":[151],"all":[152],"training).":[156]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
