{"id":"https://openalex.org/W4390306673","doi":"https://doi.org/10.48550/arxiv.2312.15663","title":"IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models","display_name":"IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4390306673","doi":"https://doi.org/10.48550/arxiv.2312.15663"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2312.15663","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2312.15663","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100341617","display_name":"Zhihao Chen","orcid":"https://orcid.org/0000-0002-6906-813X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhihao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108933348","display_name":"Bin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Bin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036916401","display_name":"Chuang Niu","orcid":"https://orcid.org/0000-0002-8475-0196"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Chuang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108085976","display_name":"Tao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Tao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100321622","display_name":"Yuxin Li","orcid":"https://orcid.org/0000-0003-2107-6744"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuxin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049086157","display_name":"Hongming Shan","orcid":"https://orcid.org/0000-0002-0604-3197"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Hongming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100400458","display_name":"Ge Wang","orcid":"https://orcid.org/0000-0002-2656-7705"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ge","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.909196,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":80,"max":85},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics in Medical Imaging Analysis","score":0.9965,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics in Medical Imaging Analysis","score":0.9965,"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/T11894","display_name":"Errors and Communication in Radiology Imaging","score":0.9863,"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":"Deep Learning in Medical Image Analysis","score":0.9778,"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/closed-captioning","display_name":"Closed captioning","score":0.91242015},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5924232},{"id":"https://openalex.org/keywords/medical-image-analysis","display_name":"Medical Image Analysis","score":0.532453},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical Imaging","score":0.520155}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.91242015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7274013},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.69674575},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5924232},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5922984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5470024},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40943155},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40205735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3637687},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2312.15663","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2312.15663","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2312.15663","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Quality education","id":"https://metadata.un.org/sdg/4","score":0.82}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4380190185","https://openalex.org/W4298897568","https://openalex.org/W4290852288","https://openalex.org/W4289422896","https://openalex.org/W3217388757","https://openalex.org/W3215212336","https://openalex.org/W3164229987","https://openalex.org/W3122720459","https://openalex.org/W2993670781","https://openalex.org/W1938708284"],"abstract_inverted_index":{"Large":[0],"language":[1,22],"models":[2,30,234],"(LLMs),":[3],"such":[4],"as":[5,19,226,228],"ChatGPT,":[6],"have":[7,36],"demonstrated":[8],"impressive":[9],"capabilities":[10,139],"in":[11,58,64],"various":[12],"tasks":[13],"and":[14,34,56,78,112,123,178,224,232],"attracted":[15],"an":[16,94,101],"increasing":[17],"interest":[18],"a":[20,118,153,202],"natural":[21],"interface":[23],"across":[24],"many":[25],"domains.":[26],"Recently,":[27],"large":[28,217],"vision-language":[29,43],"(VLMs)":[31],"like":[32],"BLIP-2":[33],"GPT-4":[35,223],"been":[37],"intensively":[38],"investigated,":[39],"which":[40,71],"learn":[41],"rich":[42,149],"correlation":[44],"from":[45],"image-text":[46],"pairs.":[47],"However,":[48],"despite":[49],"these":[50],"developments,":[51],"the":[52,138,159,165,176,187,210,229],"application":[53],"of":[54,84,140,212],"LLMs":[55],"VLMs":[57],"image":[59,96,102,160,177,197,214],"quality":[60,97,103,110,131,145,161,170,188,198,204,215],"assessment":[61,98],"(IQA),":[62],"particularly":[63],"medical":[65],"imaging,":[66],"remains":[67],"to":[68,168,195],"be":[69],"explored,":[70],"is":[72],"valuable":[73],"for":[74,108,121],"objective":[75],"performance":[76],"evaluation":[77],"potential":[79],"supplement":[80],"or":[81,200],"even":[82],"replacement":[83],"radiologists'":[85],"opinions.":[86],"To":[87,135],"this":[88,90],"end,":[89],"paper":[91],"introduces":[92],"IQAGPT,":[93],"innovative":[95],"system":[99],"integrating":[100],"captioning":[104,162,173],"VLM":[105,163],"with":[106,129,193,216],"ChatGPT":[107,194],"generating":[109],"scores":[111,146,199],"textual":[113],"reports.":[114],"First,":[115],"we":[116,142,157],"build":[117],"CT-IQA":[119,166],"dataset":[120,167],"training":[122],"evaluation,":[124],"comprising":[125],"1,000":[126],"CT":[127],"slices":[128],"diverse":[130],"levels":[132],"professionally":[133],"annotated.":[134],"better":[136],"leverage":[137],"LLMs,":[141],"convert":[143],"annotated":[144],"into":[147],"semantically":[148],"text":[150,179],"descriptions":[151],"using":[152],"prompt":[154],"template.":[155],"Second,":[156],"fine-tune":[158],"on":[164,186,238],"generate":[169],"descriptions.":[171],"The":[172],"model":[174],"fuses":[175],"features":[180],"through":[181],"cross-modal":[182],"attention.":[183],"Third,":[184],"based":[185],"descriptions,":[189],"users":[190],"can":[191],"talk":[192],"rate":[196],"produce":[201],"radiological":[203],"report.":[205],"Our":[206],"preliminary":[207],"results":[208],"demonstrate":[209],"feasibility":[211],"assessing":[213],"models.":[218],"Remarkably,":[219],"our":[220],"IQAGPT":[221],"outperforms":[222],"CLIP-IQA,":[225],"well":[227],"multi-task":[230],"classification":[231],"regression":[233],"that":[235],"solely":[236],"rely":[237],"images.":[239]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4390306673","counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2024-11-26T14:27:37.165669","created_date":"2023-12-29"}