{"id":"https://openalex.org/W4212810088","doi":"https://doi.org/10.1117/12.2607720","title":"Low-dose CT denoising via CNN trained using images with activation map","display_name":"Low-dose CT denoising via CNN trained using images with activation map","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4212810088","doi":"https://doi.org/10.1117/12.2607720"},"language":"en","primary_location":{"id":"doi:10.1117/12.2607720","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2607720","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091585847","display_name":"Minah Han","orcid":"https://orcid.org/0000-0001-9406-618X"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Minah Han","raw_affiliation_strings":["Yonsei Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Yonsei Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103089896","display_name":"Jongduk Baek","orcid":"https://orcid.org/0000-0002-2532-5413"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongduk Baek","raw_affiliation_strings":["Yonsei Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Yonsei Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5091585847"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.4292,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.5856011,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"38","last_page":"38"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9998999834060669,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10844","display_name":"Radiation Dose and Imaging","score":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7916196584701538},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.783775806427002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7271171808242798},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6742562055587769},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6735411286354065},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5351062417030334},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46884334087371826},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.44030100107192993},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4272569417953491},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4270886778831482},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4120192527770996},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3626951575279236},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3001628816127777},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15701234340667725}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7916196584701538},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.783775806427002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271171808242798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6742562055587769},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6735411286354065},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5351062417030334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46884334087371826},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.44030100107192993},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4272569417953491},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4270886778831482},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4120192527770996},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3626951575279236},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3001628816127777},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15701234340667725},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2607720","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2607720","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W2570202822","https://openalex.org/W2584483805","https://openalex.org/W2743780012","https://openalex.org/W4231200252","https://openalex.org/W4233931939","https://openalex.org/W4234552385","https://openalex.org/W4239386920","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6697443983","https://openalex.org/W6702130928"],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W2314720829","https://openalex.org/W2357322570","https://openalex.org/W90581812","https://openalex.org/W2997591215","https://openalex.org/W2227541280","https://openalex.org/W2038232617","https://openalex.org/W2005234362","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"network":[2,190],"(CNN)":[3],"based":[4],"denoisers":[5],"have":[6],"shown":[7],"promising":[8],"results":[9],"in":[10,57,147,168],"low-dose":[11],"CT":[12,119,180],"(LDCT)":[13],"denoising.":[14],"However,":[15],"image":[16,55,60,82,131,182,199],"blur":[17,56],"is":[18,50,137,156],"a":[19],"problem":[20],"that":[21,139],"needs":[22],"to":[23,86,114],"be":[24],"addressed":[25],"because":[26,61],"it":[27],"deforms":[28],"or":[29],"eliminates":[30],"small":[31,160,217],"features,":[32],"which":[33],"interferes":[34],"with":[35],"the":[36,51,54,58,62,67,81,99,102,108,153,185],"diagnosis.":[37],"Pixel":[38],"level":[39,64],"loss,":[40,45],"such":[41],"as":[42],"mean-squared-error":[43],"(MSE)":[44],"used":[46],"for":[47,91,106],"CNN":[48,93,103,112,134,166,174,193,210],"training":[49,92],"cause":[52],"of":[53,69,130,143,192],"denoised":[59],"pixel":[63,71],"loss":[65],"computes":[66],"average":[68],"all":[70],"value":[72],"differences":[73],"without":[74],"attention":[75],"on":[76],"important":[77],"features.":[78,218],"To":[79],"resolve":[80],"blur,":[83],"we":[84,172,188],"propose":[85],"use":[87],"an":[88],"activation":[89,96,128,148,154,204],"map":[90,97,129,155],"denoiser.":[94],"The":[95,206],"indicates":[98],"area":[100],"where":[101],"classifier":[104,113],"focuses":[105],"classifying":[107],"image.":[109],"We":[110,164],"train":[111,165,173],"classify":[115],"lesion-present":[116],"and":[117,125,141,150,178,197],"lesion-absent":[118],"images":[120,144],"(i.e.,":[121],"binary":[122],"detection":[123],"task),":[124],"then,":[126],"obtain":[127],"using":[132,176,195],"trained":[133,209],"classifier.":[135],"It":[136],"observed":[138],"lesions":[140],"edges":[142],"are":[145,162],"activated":[146],"map,":[149],"therefore,":[151],"when":[152],"multiplied":[157,201],"by":[158,202],"image,":[159],"features":[161],"emphasized.":[163],"denoiser":[167,175,194,211],"two":[169],"steps.":[170],"First,":[171],"LDCT":[177,196],"normal-dose":[179],"(NDCT)":[181],"pairs.":[183],"In":[184],"second":[186],"step,":[187],"fine-tune":[189],"parameters":[191],"NDCT":[198,203],"pairs":[200],"map.":[205],"two-":[207],"step":[208],"effectively":[212],"reduces":[213],"noise":[214],"while":[215],"preserving":[216]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}