{"id":"https://openalex.org/W2964296115","doi":"https://doi.org/10.1109/tmi.2019.2963248","title":"Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising","display_name":"Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising","publication_year":2019,"publication_date":"2019-12-31","ids":{"openalex":"https://openalex.org/W2964296115","doi":"https://doi.org/10.1109/tmi.2019.2963248","mag":"2964296115","pmid":"https://pubmed.ncbi.nlm.nih.gov/31902758","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/7376975"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2019.2963248","doi":"https://doi.org/10.1109/tmi.2019.2963248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2019.2963248","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7376975","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018677035","display_name":"Fenglei Fan","orcid":"https://orcid.org/0000-0003-3691-5141","observed_orcids":["https://orcid.org/0000-0003-3691-5141"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fenglei Fan","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":"https://orcid.org/0000-0003-3691-5141","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049086157","display_name":"Hongming Shan","orcid":"https://orcid.org/0000-0002-0604-3197","observed_orcids":["https://orcid.org/0000-0002-0604-3197"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongming Shan","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":"https://orcid.org/0000-0002-0604-3197","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087606400","display_name":"Mannudeep K. Kalra","orcid":"https://orcid.org/0000-0001-9938-7476","observed_orcids":["https://orcid.org/0000-0001-9938-7476"]},"institutions":[{"id":"https://openalex.org/I4210087915","display_name":"Massachusetts General Hospital","ror":"https://ror.org/002pd6e78","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210087915","https://openalex.org/I48633490"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mannudeep K. Kalra","raw_affiliation_strings":["Massachusetts General Hospital, Boston, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts General Hospital, Boston, USA","institution_ids":["https://openalex.org/I4210087915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100611912","display_name":"Ramandeep Singh","orcid":"https://orcid.org/0000-0001-5840-9907","observed_orcids":["https://orcid.org/0000-0001-5840-9907"]},"institutions":[{"id":"https://openalex.org/I4210087915","display_name":"Massachusetts General Hospital","ror":"https://ror.org/002pd6e78","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210087915","https://openalex.org/I48633490"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramandeep Singh","raw_affiliation_strings":["Massachusetts General Hospital, Boston, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts General Hospital, Boston, USA","institution_ids":["https://openalex.org/I4210087915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046833202","display_name":"Guhan Qian","orcid":"https://orcid.org/0000-0002-4993-4512","observed_orcids":["https://orcid.org/0000-0002-4993-4512"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guhan Qian","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":"https://orcid.org/0000-0002-4993-4512","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088663067","display_name":"Matthew Getzin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Getzin","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091323772","display_name":"Yueyang Teng","orcid":"https://orcid.org/0000-0002-1487-8434","observed_orcids":["https://orcid.org/0000-0002-1487-8434"]},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueyang Teng","raw_affiliation_strings":["Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-1487-8434","affiliations":[{"raw_affiliation_string":"Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073305158","display_name":"Juergen Hahn","orcid":"https://orcid.org/0000-0002-1078-4203","observed_orcids":["https://orcid.org/0000-0002-1078-4203"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juergen Hahn","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":"https://orcid.org/0000-0002-1078-4203","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100400458","display_name":"Ge Wang","orcid":"https://orcid.org/0000-0002-2656-7705","observed_orcids":["https://orcid.org/0000-0002-2656-7705","https://orcid.org/0000-0002-7516-2341"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ge Wang","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, USA"],"raw_orcid":"https://orcid.org/0000-0002-2656-7705","affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100400458"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":3.9803,"has_fulltext":false,"cited_by_count":159,"citation_normalized_percentile":{"value":0.94895187,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"39","issue":"6","first_page":"2035","last_page":"2050"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940999746322632,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940999746322632,"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/T10320","display_name":"Neural Networks and Applications","score":0.0017999999690800905,"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/T11019","display_name":"Image Enhancement Techniques","score":0.0010000000474974513,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/low-dose-ct-denoising","display_name":"low-dose CT denoising","score":0.9079999923706055},{"id":"https://openalex.org/keywords/encoder-decoder-architecture","display_name":"encoder-decoder architecture","score":0.7490000128746033},{"id":"https://openalex.org/keywords/artificial-neurons","display_name":"artificial neurons","score":0.5649999976158142},{"id":"https://openalex.org/keywords/image-denoising","display_name":"image denoising","score":0.7149999737739563},{"id":"https://openalex.org/keywords/model-efficiency","display_name":"model efficiency","score":0.699999988079071},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.6079999804496765}],"concepts":[{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.6765958666801453},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6761153340339661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.550902783870697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5486557483673096},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5428093075752258},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.5207550525665283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49880146980285645},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4886780083179474},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4792045056819916},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4463542401790619},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42631879448890686},{"id":"https://openalex.org/C166437778","wikidata":"https://www.wikidata.org/wiki/Q50695","display_name":"Quadratic function","level":3,"score":0.41025641560554504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39733651280403137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30192577838897705},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.21144002676010132},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009474","descriptor_name":"Neurons","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1109/tmi.2019.2963248","doi":"https://doi.org/10.1109/tmi.2019.2963248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2019.2963248","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:31902758","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31902758","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:7376975","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7376975","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"IEEE Trans Med Imaging","raw_type":"Text"},{"id":"pmh:oai:arXiv.org:1901.05593","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.05593","pdf_url":"https://arxiv.org/pdf/1901.05593","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1901.05593","doi":"https://doi.org/10.48550/arxiv.1901.05593","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1901.05593","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7376975","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7376975","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"IEEE Trans Med Imaging","raw_type":"Text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1719434482","display_name":"Focused-x-ray Luminescence at uCT Resolution and uM-level Sensitivity","funder_award_id":"R01EB026646","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"},{"id":"https://openalex.org/G5929270558","display_name":"Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases","funder_award_id":"R01CA233888","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G7153341714","display_name":"Photon-counting X-ray and Optical Tomography for Preclinical Cancer Research","funder_award_id":"R01CA237267","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"}],"funders":[{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"},{"id":"https://openalex.org/F4320337363","display_name":"National Institute of Biomedical Imaging and Bioengineering","ror":"https://ror.org/00372qc85"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1853900790","https://openalex.org/W1959608418","https://openalex.org/W1964231221","https://openalex.org/W1972150100","https://openalex.org/W2002611249","https://openalex.org/W2041114617","https://openalex.org/W2044774090","https://openalex.org/W2063061288","https://openalex.org/W2086789740","https://openalex.org/W2094366314","https://openalex.org/W2099471712","https://openalex.org/W2100495367","https://openalex.org/W2143163922","https://openalex.org/W2143859313","https://openalex.org/W2145094598","https://openalex.org/W2160547390","https://openalex.org/W2163605009","https://openalex.org/W2165698076","https://openalex.org/W2171697262","https://openalex.org/W2194775991","https://openalex.org/W2218318129","https://openalex.org/W2542870090","https://openalex.org/W2570202822","https://openalex.org/W2574952845","https://openalex.org/W2584321260","https://openalex.org/W2584483805","https://openalex.org/W2604414494","https://openalex.org/W2605801757","https://openalex.org/W2613053618","https://openalex.org/W2617128058","https://openalex.org/W2743780012","https://openalex.org/W2746715616","https://openalex.org/W2787564202","https://openalex.org/W2793419304","https://openalex.org/W2798401174","https://openalex.org/W2807741195","https://openalex.org/W2812986998","https://openalex.org/W2886084313","https://openalex.org/W2914361849","https://openalex.org/W2919115771","https://openalex.org/W2962709635","https://openalex.org/W2963446712","https://openalex.org/W2963731047","https://openalex.org/W2963911037","https://openalex.org/W2964121744","https://openalex.org/W2964148965","https://openalex.org/W2964149935","https://openalex.org/W2973835018","https://openalex.org/W2997574889","https://openalex.org/W2999571325","https://openalex.org/W3038022805","https://openalex.org/W3098281398","https://openalex.org/W4255885670","https://openalex.org/W4296142362","https://openalex.org/W4320013936","https://openalex.org/W6638444622","https://openalex.org/W6736332608"],"related_works":["https://openalex.org/W2997375529","https://openalex.org/W2972547053","https://openalex.org/W2791923256","https://openalex.org/W7204013285","https://openalex.org/W3163625948","https://openalex.org/W4409311346","https://openalex.org/W2767768766","https://openalex.org/W2886851666","https://openalex.org/W2527841944","https://openalex.org/W4417368801"],"abstract_inverted_index":{"Inspired":[0],"by":[1,13],"complexity":[2],"and":[3,75,80,99,118,128,153],"diversity":[4],"of":[5,33,47,60,72,120,125,151],"biological":[6],"neurons,":[7],"our":[8,132],"group":[9],"proposed":[10],"quadratic":[11,24,48,73,87,97,121],"neurons":[12,21,49,74,88,152],"replacing":[14],"the":[15,31,45,58,96,110,116,137,141,159],"inner":[16],"product":[17],"in":[18,50,57,77,123,158],"current":[19],"artificial":[20],"with":[22,147],"a":[23,148,155],"operation":[25],"on":[26,109],"input":[27],"data,":[28],"thereby":[29],"enhancing":[30],"capability":[32],"an":[34,91],"individual":[35],"neuron.":[36],"Along":[37],"this":[38,83,135],"direction,":[39],"we":[40,85],"are":[41],"motivated":[42],"to":[43,89,102],"evaluate":[44],"power":[46],"popular":[51],"network":[52],"architectures,":[53],"simulating":[54],"human-like":[55],"learning":[56,143],"form":[59],"\"quadratic-neuron-based":[61],"deep":[62,142],"learning\".":[63],"Our":[64],"prior":[65],"theoretical":[66],"studies":[67],"have":[68],"shown":[69],"important":[70],"merits":[71],"networks":[76],"representation,":[78],"efficiency,":[79],"interpretability.":[81],"In":[82],"paper,":[84],"use":[86],"construct":[90],"encoder-decoder":[92],"structure,":[93],"referred":[94],"as":[95],"autoencoder,":[98],"apply":[100],"it":[101],"low-dose":[103,112],"CT":[104,113],"denoising.":[105],"The":[106],"experimental":[107],"results":[108],"Mayo":[111],"dataset":[114],"demonstrate":[115],"utility":[117],"robustness":[119],"autoencoder":[122],"terms":[124],"image":[126],"denoising":[127],"model":[129],"efficiency.":[130],"To":[131],"best":[133],"knowledge,":[134],"is":[136,145],"first":[138],"time":[139],"that":[140],"approach":[144],"implemented":[146],"new":[149],"type":[150],"demonstrates":[154],"significant":[156],"potential":[157],"medical":[160],"imaging":[161],"field.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":19},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":40},{"year":2023,"cited_by_count":35},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2019-07-30T00:00:00"}
