{"id":"https://openalex.org/W7124144508","doi":"https://doi.org/10.1145/3777577.3777668","title":"Self-Supervised Terahertz medical Image Denoising Based on Attention Mechanism and DnCNN","display_name":"Self-Supervised Terahertz medical Image Denoising Based on Attention Mechanism and DnCNN","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W7124144508","doi":"https://doi.org/10.1145/3777577.3777668"},"language":null,"primary_location":{"id":"doi:10.1145/3777577.3777668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777668","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3777577.3777668","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123008725","display_name":"Ting Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ting Tan","raw_affiliation_strings":["School of Mathematics, Sichuan University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sichuan University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081802888","display_name":"Y. X. Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yigong Tan","raw_affiliation_strings":["School of Mathematics, Sichuan University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sichuan University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123019097","display_name":"Yue Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhong","raw_affiliation_strings":["School of Mathematics, Sichuan University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sichuan University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123019008","display_name":"Ke Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Deng","raw_affiliation_strings":["School of Mathematics, Sichuan University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Sichuan University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5123008725"],"corresponding_institution_ids":["https://openalex.org/I24185976"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61781237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"554","last_page":"560"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10752","display_name":"Terahertz technology and applications","score":0.6069999933242798,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10752","display_name":"Terahertz technology and applications","score":0.6069999933242798,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.18610000610351562,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.02070000022649765,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.67330002784729},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5795000195503235},{"id":"https://openalex.org/keywords/terahertz-radiation","display_name":"Terahertz radiation","score":0.5580999851226807},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5239999890327454},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5134999752044678},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.460099995136261},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4514999985694885},{"id":"https://openalex.org/keywords/attenuation","display_name":"Attenuation","score":0.40630000829696655}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7638999819755554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.67330002784729},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.67330002784729},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5795000195503235},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5580999851226807},{"id":"https://openalex.org/C107816215","wikidata":"https://www.wikidata.org/wiki/Q647887","display_name":"Terahertz radiation","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.460099995136261},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C184652730","wikidata":"https://www.wikidata.org/wiki/Q2357982","display_name":"Attenuation","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3431999981403351},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.34310001134872437},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.3294000029563904},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C2987933465","wikidata":"https://www.wikidata.org/wiki/Q141130","display_name":"Image manipulation","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777577.3777668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777668","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3777577.3777668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777668","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2056370875","https://openalex.org/W2133665775","https://openalex.org/W2508457857","https://openalex.org/W2613155248","https://openalex.org/W2764207251","https://openalex.org/W2866634454","https://openalex.org/W2884585870","https://openalex.org/W2902857081","https://openalex.org/W2963420686","https://openalex.org/W3109987498","https://openalex.org/W4307821374","https://openalex.org/W4316021039","https://openalex.org/W4323338593","https://openalex.org/W4403110117","https://openalex.org/W4412632709"],"related_works":[],"abstract_inverted_index":{"Although":[0],"terahertz":[1],"(THz)":[2],"medical":[3,36,143],"imaging":[4],"is":[5],"advantageous":[6],"owing":[7],"to":[8,73,96,129],"its":[9],"non-ionizing":[10],"nature":[11],"and":[12,22,68,100,105,121,169,188,201],"molecular":[13],"sensitivity,":[14],"signal":[15],"attenuation":[16],"resulting":[17],"from":[18,49,63,156],"high":[19],"water":[20],"content":[21],"diverse":[23],"noise":[24],"sources":[25],"necessitates":[26],"effective":[27],"denoising.":[28,38],"Deep":[29],"learning":[30],"models":[31,41,54,159],"demonstrate":[32,163],"notable":[33],"advantages":[34],"in":[35,167],"image":[37,70,135,144],"However,":[39],"supervised":[40],"rely":[42],"on":[43,139,198],"paired":[44,59,199],"clean-noisy":[45],"data,":[46],"which":[47,90,114,146],"deviates":[48],"clinical":[50],"reality,":[51],"while":[52,191],"self-supervised":[53,112],"eliminate":[55],"the":[56,86,107,117,126,130,133,181,192],"need":[57],"for":[58,111],"data":[60,200],"but":[61],"suffer":[62],"a":[64],"discrepancy":[65],"between":[66,103,119],"learned":[67],"real-world":[69],"priors,":[71],"leading":[72],"performance":[74],"degradation.":[75],"To":[76],"overcome":[77],"these":[78],"limitations,":[79],"this":[80],"paper":[81],"enhances":[82,186],"DnCNN":[83],"by":[84,124],"integrating":[85],"Squeeze-and-Excitation":[87],"(SE)":[88],"block,":[89],"adaptively":[91],"recalibrates":[92],"channel-wise":[93],"feature":[94],"responses":[95],"emphasize":[97],"informative":[98],"regions":[99],"improve":[101],"contrast":[102],"structures,":[104],"employs":[106],"Noisy-As-Clean":[108],"(NAC)":[109],"strategy":[110,194],"training,":[113],"effectively":[115,185],"narrows":[116],"disparity":[118],"training":[120],"test":[122,134],"conditions":[123],"adapting":[125],"model":[127],"directly":[128],"characteristics":[131],"of":[132,152],"itself.":[136],"Experimental":[137],"results":[138,178],"our":[140],"own":[141],"THz":[142,161],"dataset,":[145],"comprises":[147],"320":[148],"PNG":[149],"format":[150],"images":[151],"369\u00d7369":[153],"pixels":[154],"generated":[155],"3D-printed":[157],"tooth":[158],"using":[160],"imaging,":[162],"significant":[164],"quantitative":[165],"improvements":[166],"PSNR":[168],"SSIM":[170],"compared":[171],"with":[172],"other":[173],"renowned":[174],"denoising":[175,204],"algorithms.":[176],"These":[177],"confirm":[179],"that":[180],"integrated":[182],"SE":[183],"block":[184],"edge":[187],"texture":[189],"preservation,":[190],"NAC":[193],"substantially":[195],"reduces":[196],"dependency":[197],"boosts":[202],"overall":[203],"performance.":[205]},"counts_by_year":[],"updated_date":"2026-01-15T23:21:31.212559","created_date":"2026-01-15T00:00:00"}