{"id":"https://openalex.org/W3202232410","doi":"https://doi.org/10.1145/3478905.3478940","title":"Model Compression with NAS and Knowledge Distillation for Medical Image Segmentation","display_name":"Model Compression with NAS and Knowledge Distillation for Medical Image Segmentation","publication_year":2021,"publication_date":"2021-07-23","ids":{"openalex":"https://openalex.org/W3202232410","doi":"https://doi.org/10.1145/3478905.3478940","mag":"3202232410"},"language":"en","primary_location":{"id":"doi:10.1145/3478905.3478940","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3478905.3478940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Data Science and Information Technology","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/A5051044069","display_name":"Zhong Zheng","orcid":"https://orcid.org/0000-0002-1741-0554"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhong Zheng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085386009","display_name":"Guixia Kang","orcid":"https://orcid.org/0000-0002-4039-4505"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guixia Kang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051044069"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.1818,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51537783,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"173","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9965999722480774,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9962999820709229,"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/computer-science","display_name":"Computer science","score":0.7668347954750061},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6746921539306641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6587320566177368},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.629459023475647},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5778462290763855},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.47554585337638855},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.461958646774292},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45864325761795044},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4421975016593933},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42819955945014954},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4052772521972656},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39797472953796387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3742712736129761}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7668347954750061},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6746921539306641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6587320566177368},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.629459023475647},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5778462290763855},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.47554585337638855},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.461958646774292},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45864325761795044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4421975016593933},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42819955945014954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4052772521972656},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39797472953796387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3742712736129761}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3478905.3478940","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3478905.3478940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Data Science and Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4300000071525574,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W2028344184","https://openalex.org/W3007268491","https://openalex.org/W3035204081"],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4205800335","https://openalex.org/W2055202857","https://openalex.org/W2022929107","https://openalex.org/W80586315"],"abstract_inverted_index":{"Medical":[0],"image":[1,62,75,90,277,295],"segmentation":[2,36,57,63,76,91,148,157,278,296,306],"task":[3],"has":[4,78],"been":[5,43,52,80],"a":[6,88,109,137,207],"hot":[7],"research":[8],"topic":[9],"in":[10,54,126,139,174,209],"the":[11,23,69,73,114,127,152,156,165,187,195,210,223,232,236,240,244,249,253,257,291],"field":[12,18],"of":[13,28,60,72,116,155,171,225,266,281,293,305],"computer":[14],"vision":[15],"and":[16,26,33,45,49,129,146,168,239,256,284,310],"natural":[17],"for":[19,191,203,216,228],"many":[20,55],"years.":[21],"With":[22],"rapid":[24],"development":[25],"application":[27],"convolutional":[29,101,267],"neural":[30,102,132],"networks,":[31],"more":[32,34,46],"medical":[35,61,74,89,294],"models":[37,279,297],"based":[38,112],"on":[39,113],"deep":[40],"learning":[41,285],"have":[42,51],"proposed,":[44],"successful":[47],"results":[48],"applications":[50],"achieved":[53],"disease":[56],"tasks.The":[58],"effect":[59],"tasks":[64],"is":[65,96,124,182,213],"getting":[66],"better.":[67],"However,":[68],"computational":[70],"cost":[71],"model":[77,92,234,238,246,255,282],"not":[79],"improved":[81],"well.":[82],"In":[83],"this":[84,140],"work,":[85],"we":[86,105,159,199,221,261],"propose":[87,160],"compression":[93],"scheme":[94],"that":[95,164],"theoretically":[97],"applicable":[98],"to":[99,107,135,162,185,193,247,274],"all":[100],"networks.":[103],"First,":[104],"choose":[106],"construct":[108],"search":[110,134,141,176,189,211],"space":[111,142,177,190,212],"number":[115],"convolution":[117,123,264],"kernels":[118],"at":[119,151],"each":[120,218],"location":[121],"where":[122],"used":[125],"model,":[128],"then":[130],"use":[131,200,222,231,262],"network":[133],"find":[136,194],"sub-network":[138,173,208,242],"with":[143,302],"less":[144],"computation":[145,292],"high":[147],"accuracy.":[149],"Aiming":[150],"encoding-decoding":[153],"structure":[154,167,170],"network,":[158],"Symmetrical-NAS":[161],"ensure":[163],"encoding":[166],"decoding":[169],"any":[172],"our":[175],"are":[178],"symmetrical.":[179],"Since":[180],"it":[181],"too":[183],"expensive":[184],"traverse":[186],"entire":[188],"training":[192],"most":[196],"suitable":[197],"architecture,":[198],"weight":[201],"sharing":[202],"training.":[204,229],"During":[205],"training,":[206],"randomly":[214],"selected":[215],"activation":[217],"time.":[219],"Second,":[220],"method":[224,270,288],"knowledge":[226,250],"distillation":[227],"We":[230],"basic":[233],"as":[235,243],"teacher":[237,254],"searched":[241],"student":[245,258],"realize":[248],"transfer":[251],"between":[252],"model.":[259],"Third,":[260],"separable":[263],"instead":[265],"layer.":[268],"Our":[269,287],"can":[271,289],"be":[272,313],"applied":[273],"various":[275],"medicial":[276],"regardless":[280],"architectures":[283],"algorithms.":[286],"reduce":[290],"by":[298],"90\u00d7":[299],"regarding":[300],"FLOPs":[301],"little":[303],"loss":[304],"effect.":[307],"The":[308],"code":[309],"demo":[311],"will":[312],"publicly":[314],"available.":[315]},"counts_by_year":[{"year":2025,"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"}