{"id":"https://openalex.org/W6961893714","doi":"https://doi.org/10.15167/vincenzi-elena_phd2024-04-19","title":"Deep Learning Applications in Pre-Clinical Imaging for accelerated Drug Discovery Studies","display_name":"Deep Learning Applications in Pre-Clinical Imaging for accelerated Drug Discovery Studies","publication_year":2024,"publication_date":"2024-04-19","ids":{"openalex":"https://openalex.org/W6961893714","doi":"https://doi.org/10.15167/vincenzi-elena_phd2024-04-19"},"language":"en","primary_location":{"id":"pmh:oai:iris.unige.it:11567/1170959","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1170959","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11567/1170959","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"VINCENZI, ELENA","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"VINCENZI, ELENA","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"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":true,"primary_topic":{"id":"https://openalex.org/T12771","display_name":"Plant pathogens and resistance mechanisms","score":0.574400007724762,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12771","display_name":"Plant pathogens and resistance mechanisms","score":0.574400007724762,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13125","display_name":"Agricultural pest management studies","score":0.07959999889135361,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13506","display_name":"Botanical Research and Chemistry","score":0.06069999933242798,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8496999740600586},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6310999989509583},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.604200005531311},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4578000009059906},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.44359999895095825},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4408000111579895},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.41359999775886536}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8496999740600586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.729200005531311},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.604200005531311},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5521000027656555},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4408000111579895},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.41359999775886536},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3926999866962433},{"id":"https://openalex.org/C64903051","wikidata":"https://www.wikidata.org/wiki/Q2198549","display_name":"Drug development","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35499998927116394},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.33009999990463257},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C3019060180","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automated method","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2623000144958496}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:iris.unige.it:11567/1170959","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1170959","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.15167/vincenzi-elena_phd2024-04-19","is_oa":true,"landing_page_url":"https://doi.org/10.15167/vincenzi-elena_phd2024-04-19","pdf_url":null,"source":{"id":"https://openalex.org/S7407050993","display_name":"Universit\u00e0 degli Studi di Genova","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:iris.unige.it:11567/1170959","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1170959","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"development":[1],"of":[2,21,55,108,113,120,143,163,199,208,229,236,245,294,308,321,326,331,342,371,380,386,389],"new":[3,24],"drugs,":[4],"preclinical":[5,40,66,411],"studies":[6,89],"based":[7],"on":[8,139],"animal":[9],"models":[10,388],"are":[11],"essential":[12],"to":[13,30,83,127,159,172,192,217,226,251,257,279,287,311,323,345,360,364],"understand":[14],"the":[15,19,22,39,106,111,140,178,197,203,230,234,253,258,272,291,301,319,332,352,368,404],"pathogenesis":[16],"and":[17,57,74,81,100,148,150,165,184,267,290,358,383,398],"assess":[18],"efficacy":[20],"identified":[23],"drug.":[25],"As":[26],"technological":[27],"advancements":[28],"continue":[29],"unfold,":[31],"medical":[32],"imaging":[33,67,145,334],"is":[34,119,157,169,190,215],"gaining":[35],"significant":[36],"prominence":[37],"in":[38,49,65,95,105,202,317,410],"domain":[41],"as":[42,123],"well.":[43],"Having":[44],"already":[45],"shown":[46],"state-of-art":[47],"performaces":[48],"clinical":[50],"imaging,":[51,154,212],"automated":[52,141,378,394],"tools":[53,349],"capable":[54],"quickly":[56],"effectively":[58],"processing":[59,77],"images":[60,385],"can":[61],"offer":[62],"invaluable":[63],"assistance":[64],"endeavors.&#13;\\nIn":[68],"this":[69,134,136],"thesis,":[70],"deep":[71,155,213,302],"learning":[72,156,214,303],"(DL)":[73],"classical":[75],"image":[76],"methodologies":[78],"were":[79],"designed":[80],"developed":[82,273],"support":[84],"pharmacologists":[85],"during":[86],"drug":[87,109],"discovery":[88],"for":[90,355,377,406],"idiophathic":[91],"pulmonary":[92,391],"fibrosis":[93,322],"(IPF)":[94],"both":[96],"in-vivo":[97,266],"(Micro-CT":[98],"imaging)":[99,103],"ex-vivo":[101,268],"(histological":[102],"analysis.Particularly":[104],"context":[107],"research,":[110],"integration":[112],"heterogeneous":[114],"data":[115,400],"from":[116,130],"different":[117,144,324],"sources":[118],"particular":[121],"interest,":[122],"it":[124],"allows":[125],"phenomena":[126],"be":[128,361,365],"analyzed":[129],"multiple":[131],"perspectives.":[132],"For":[133],"reason,":[135],"thesis":[137],"focuses":[138],"analysis":[142,379],"modalities":[146,335],"(micro-ct":[147],"histology)":[149],"their":[151],"integration.&#13;\\nIn":[152],"Micro-CT":[153,270],"exploited":[158],"perform":[160],"automatic":[161],"segmentation":[162,168,189],"lungs":[164],"airways.":[166],"Lung":[167,282],"then":[170],"used":[171,191,216],"extract":[173,193],"lung":[174,246,327],"densitometry":[175,283],"by":[176,222],"exploiting":[177],"linear":[179],"relationship":[180],"between":[181,265],"X-ray":[182],"attenuation":[183],"tissue":[185],"density,":[186],"while":[187],"airways":[188,204,295],"geometric":[194,292],"measurements,":[195],"facilitating":[196],"assessment":[198,293],"morphological":[200],"changes":[201],"at":[205],"various":[206],"stages":[207],"IPF":[209],"development.&#13;\\nIn":[210],"histological":[211,299,384],"create":[218],"a":[219,224,248,306,340,375],"local":[220],"map":[221],"assigning":[223,318],"score":[225],"each":[227],"patch":[228],"created":[231],"tesselation,":[232],"representing":[233],"severity":[235],"fibrosis.":[237,392],"Finally,":[238],"since":[239],"histology":[240],"involves":[241],"less":[242],"than":[243],"$1\\\\%$":[244],"volume,":[247],"semi-automated":[249],"approach":[250],"identify":[252],"micro-CT":[254,381],"slice":[255,260],"corresponding":[256],"histologic":[259],"was":[261],"implemented,":[262],"enabling":[263],"comparisons":[264],"evaluations.&#13;\\n&#13;\\nFor":[269],"images,":[271,300],"pipeline":[274],"provided":[275],"better":[276],"results":[277,285,338],"compared":[278],"state-of-the-art":[280],"approaches.":[281],"showed":[284,336],"comparable":[286,344],"manual":[288,315,356],"evaluations,":[289],"yielded":[296],"promising":[297,337],"results.&#13;\\nRegarding":[298],"model":[304],"achieved":[305],"level":[307,341],"accuracy":[309,343],"similar":[310],"that":[312],"obtained":[313],"through":[314],"assessments":[316],"degree":[320],"areas":[325],"tissue.":[328],"&#13;\\nThe":[329],"matching":[330],"two":[333],"with":[339],"human":[346],"error.&#13;\\nAll":[347],"three":[348],"significantly":[350],"reduced":[351],"time":[353],"required":[354],"analyses":[357],"proved":[359],"effective":[362],"enough":[363],"integrated":[366],"into":[367],"routine":[369],"work":[370,373],"pharmacologists.\\\\\\\\&#13;\\n&#13;\\nThis":[372],"presents":[374],"platform":[376],"scans":[382],"mouse":[387],"idiopathic":[390],"This":[393],"tool":[395],"enables":[396],"rapid":[397],"comprehensive":[399],"analysis,":[401],"thus":[402],"laying":[403],"foundation":[405],"its":[407],"wider":[408],"use":[409],"settings.":[412]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
