{"id":"https://openalex.org/W4225596864","doi":"https://doi.org/10.1109/isbi52829.2022.9761698","title":"An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans","display_name":"An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans","publication_year":2022,"publication_date":"2022-03-28","ids":{"openalex":"https://openalex.org/W4225596864","doi":"https://doi.org/10.1109/isbi52829.2022.9761698"},"language":"en","primary_location":{"id":"doi:10.1109/isbi52829.2022.9761698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761698","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":"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/A5044067631","display_name":"Manu Sheoran","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Manu Sheoran","raw_affiliation_strings":["TCS Research,New Delhi,India","TCS Research, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research,New Delhi,India","institution_ids":[]},{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081336490","display_name":"Meghal Dani","orcid":"https://orcid.org/0009-0007-1249-3630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meghal Dani","raw_affiliation_strings":["TCS Research,New Delhi,India","TCS Research, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research,New Delhi,India","institution_ids":[]},{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101897117","display_name":"Monika Sharma","orcid":"https://orcid.org/0000-0002-7346-2711"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monika Sharma","raw_affiliation_strings":["TCS Research,New Delhi,India","TCS Research, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research,New Delhi,India","institution_ids":[]},{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071894271","display_name":"Lovekesh Vig","orcid":"https://orcid.org/0000-0001-9834-3308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lovekesh Vig","raw_affiliation_strings":["TCS Research,New Delhi,India","TCS Research, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research,New Delhi,India","institution_ids":[]},{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5044067631"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4768,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7238075,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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":0.9987999796867371,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9980999827384949,"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":0.9979000091552734,"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/initialization","display_name":"Initialization","score":0.9240811467170715},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8268239498138428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6097238659858704},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5549024939537048},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.546924352645874},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4718190133571625},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.46771541237831116},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.4321076273918152},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35451099276542664}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.9240811467170715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8268239498138428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6097238659858704},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5549024939537048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.546924352645874},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4718190133571625},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.46771541237831116},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.4321076273918152},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35451099276542664},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi52829.2022.9761698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi52829.2022.9761698","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2565639579","https://openalex.org/W2592929672","https://openalex.org/W2883683269","https://openalex.org/W2884584803","https://openalex.org/W2890179236","https://openalex.org/W2953106684","https://openalex.org/W2979362285","https://openalex.org/W2979564444","https://openalex.org/W2980091915","https://openalex.org/W2982770724","https://openalex.org/W2999246307","https://openalex.org/W3030252702","https://openalex.org/W3035060554","https://openalex.org/W3094502228","https://openalex.org/W3101394124","https://openalex.org/W3119668554","https://openalex.org/W3157361989","https://openalex.org/W6620707391","https://openalex.org/W6632937448","https://openalex.org/W6735463952","https://openalex.org/W6778574831","https://openalex.org/W6779326418","https://openalex.org/W6784333009"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W3095877357","https://openalex.org/W183202219","https://openalex.org/W10861731","https://openalex.org/W2072565696","https://openalex.org/W2050451745"],"abstract_inverted_index":{"Existing":[0],"universal":[1],"lesion":[2,48],"detection":[3,19,49],"(ULD)":[4],"methods":[5],"utilize":[6],"compute-intensive":[7],"anchor-based":[8],"architectures":[9],"which":[10,143],"rely":[11],"on":[12,73,139],"predefined":[13],"anchor":[14],"boxes,":[15],"resulting":[16],"in":[17,22,99],"unsatisfactory":[18],"performance,":[20],"especially":[21],"small":[23],"and":[24,32,115],"mid-sized":[25],"lesions.":[26],"Further,":[27],"these":[28],"default":[29],"fixed":[30],"anchor-sizes":[31],"ratios":[33],"do":[34],"not":[35],"generalize":[36],"well":[37,54],"to":[38,129],"different":[39],"datasets.":[40],"Therefore,":[41],"we":[42,84],"propose":[43],"a":[44],"robust":[45],"one-stage":[46],"anchor-free":[47],"network":[50],"that":[51,63,86],"can":[52,67,89],"perform":[53],"across":[55,152],"varying":[56],"lesions":[57,150],"sizes":[58],"by":[59,92,111],"exploiting":[60],"the":[61,64,81,87,96,100,130,140],"fact":[62],"box":[65],"predictions":[66],"be":[68,90],"sorted":[69],"for":[70],"relevance":[71],"based":[72,113],"their":[74,78],"center":[75],"rather":[76],"than":[77],"overlap":[79],"with":[80,149],"object.":[82],"Furthermore,":[83],"demonstrate":[85],"ULD":[88],"improved":[91],"explicitly":[93],"providing":[94],"it":[95],"domain-specific":[97],"information":[98],"form":[101],"of":[102,137,145],"multi-intensity":[103],"images":[104],"generated":[105],"using":[106,118],"multiple":[107],"HU":[108],"windows,":[109],"followed":[110],"self-attention":[112],"feature-fusion":[114],"backbone":[116],"initialization":[117],"weights":[119],"learned":[120],"via":[121],"self-supervision":[122],"over":[123],"CT-scans.":[124],"We":[125],"obtain":[126],"comparable":[127],"results":[128],"state-of-the-art":[131],"methods,":[132],"achieving":[133],"an":[134],"overall":[135],"sensitivity":[136],"86.05%":[138],"DeepLesion":[141],"dataset,":[142],"comprises":[144],"approximately":[146],"32K":[147],"CT-scans":[148],"annotated":[151],"various":[153],"body":[154],"organs.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}
