{"id":"https://openalex.org/W7131380625","doi":"https://doi.org/10.48550/arxiv.2602.20845","title":"FLIM Networks with Bag of Feature Points","display_name":"FLIM Networks with Bag of Feature Points","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131380625","doi":"https://doi.org/10.48550/arxiv.2602.20845"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.20845","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","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/A5126848922","display_name":"Jo\u00e3o Deltregia Martinelli","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Martinelli, Jo\u00e3o Deltregia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014585339","display_name":"Marcelo Luis Rodrigues Filho","orcid":"https://orcid.org/0009-0002-3848-4795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Filho, Marcelo Luis Rodrigues","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126837773","display_name":"Felipe Crispim da Rocha Salvagnini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salvagnini, Felipe Crispim da Rocha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126794357","display_name":"Gilson Junior Soares","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soares, Gilson Junior","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126797644","display_name":"Jefersson A. dos Santos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Santos, Jefersson A. dos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015267493","display_name":"Alexandre X. Falc\u00e3o","orcid":"https://orcid.org/0000-0002-2914-5380"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Falc\u00e3o, Alexandre X.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5126848922"],"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.3255999982357025,"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":0.3255999982357025,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.22830000519752502,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.20080000162124634,"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/feature","display_name":"Feature (linguistics)","score":0.6897000074386597},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6456000208854675},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6262000203132629},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5110999941825867},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4851999878883362},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4779999852180481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4440999925136566},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3919000029563904},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.38449999690055847}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7793999910354614},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6897000074386597},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725000143051147},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6456000208854675},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6262000203132629},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5608999729156494},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3560999929904938},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3208000063896179},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.29429998993873596},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.20845","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.20845","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20845","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.2602.20845","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7897833585739136,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Convolutional":[0],"networks":[1],"require":[2],"extensive":[3],"image":[4],"annotation,":[5],"which":[6],"can":[7],"be":[8],"costly":[9],"and":[10,81,111,136,155,162],"time-consuming.":[11],"Feature":[12,85],"Learning":[13],"from":[14,28,140],"Image":[15],"Markers":[16],"(FLIM)":[17],"tackles":[18],"this":[19,119],"challenge":[20],"by":[21,121],"estimating":[22],"encoder":[23,44],"filters":[24,99,138],"(i.e.,":[25],"kernel":[26],"weights)":[27],"user-drawn":[29],"markers":[30],"on":[31],"discriminative":[32],"regions":[33],"of":[34,84,133,157],"a":[35,51,88,123,131],"few":[36],"representative":[37],"images":[38],"without":[39,56],"traditional":[40],"optimization.":[41],"Such":[42],"an":[43,47],"combined":[45],"with":[46],"adaptive":[48],"decoder":[49],"comprises":[50],"FLIM":[52,79],"network":[53],"fully":[54],"trained":[55],"backpropagation.":[57],"Prior":[58],"research":[59],"has":[60],"demonstrated":[61],"their":[62],"effectiveness":[63],"in":[64,152,169],"Salient":[65],"Object":[66],"Detection":[67],"(SOD),":[68],"being":[69],"significantly":[70],"lighter":[71],"than":[72],"existing":[73],"lightweight":[74],"models.":[75],"This":[76],"study":[77],"revisits":[78],"SOD":[80],"introduces":[82],"FLIM-Bag":[83],"Points":[86],"(FLIM-BoFP),":[87],"considerably":[89],"faster":[90],"filter":[91,115],"estimation":[92],"method.":[93],"The":[94,147],"previous":[95],"approach,":[96],"FLIM-Cluster,":[97],"derives":[98],"through":[100],"patch":[101],"clustering":[102,125],"at":[103,126],"each":[104],"encoder's":[105],"block,":[106,129],"leading":[107],"to":[108,160],"computational":[109],"overhead":[110],"reduced":[112],"control":[113],"over":[114],"locations.":[116],"FLIM-BoFP":[117,158],"streamlines":[118],"process":[120],"performing":[122],"single":[124],"the":[127,150],"input":[128],"creating":[130],"bag":[132],"feature":[134,142],"points,":[135],"defining":[137],"directly":[139],"mapped":[141],"points":[143],"across":[144],"all":[145],"blocks.":[146],"paper":[148],"evaluates":[149],"benefits":[151],"efficiency,":[153],"effectiveness,":[154],"generalization":[156],"compared":[159],"FLIM-Cluster":[161],"other":[163],"state-of-the-art":[164],"baselines":[165],"for":[166],"parasite":[167],"detection":[168],"optical":[170],"microscopy":[171],"images.":[172]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-26T00:00:00"}