{"id":"https://openalex.org/W2996232652","doi":"https://doi.org/10.1109/tencon.2019.8929585","title":"Opti-QIBDS Net: A Quantum-Inspired Optimized Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation","display_name":"Opti-QIBDS Net: A Quantum-Inspired Optimized Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2996232652","doi":"https://doi.org/10.1109/tencon.2019.8929585","mag":"2996232652"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5018715621","display_name":"Debanjan Konar","orcid":"https://orcid.org/0000-0002-7423-9319"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Debanjan Konar","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005258575","display_name":"Siddhartha Bhattacharyya","orcid":"https://orcid.org/0000-0003-0360-7919"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siddhartha Bhattacharyya","raw_affiliation_strings":["Department of Information Technology, RCC Institute of Information Technology, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, RCC Institute of Information Technology, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017878614","display_name":"Sandip Dey","orcid":"https://orcid.org/0000-0002-1005-3304"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandip Dey","raw_affiliation_strings":["Department of Computer Science and Engineering, Sukanta Mahavidyalaya, Jalpaiguri, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sukanta Mahavidyalaya, Jalpaiguri, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073634264","display_name":"Bijaya Ketan Panigrahi","orcid":"https://orcid.org/0000-0003-2062-2889"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bijaya Ketan Panigrahi","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5018715621"],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":1.1803,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.79685692,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"761","last_page":"766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9969000220298767,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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":0.7009371519088745},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6122759580612183},{"id":"https://openalex.org/keywords/quantum-computer","display_name":"Quantum computer","score":0.5949934720993042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.588467001914978},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.570362389087677},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5170729756355286},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5149539113044739},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.4607464075088501},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.45930489897727966},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4575745761394501},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.4411343038082123},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4350331425666809},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.4257577061653137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40676188468933105},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33815205097198486},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3366600275039673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20090609788894653},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15025213360786438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009371519088745},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6122759580612183},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.5949934720993042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.588467001914978},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.570362389087677},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5170729756355286},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5149539113044739},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.4607464075088501},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.45930489897727966},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4575745761394501},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.4411343038082123},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4350331425666809},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.4257577061653137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40676188468933105},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33815205097198486},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3366600275039673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20090609788894653},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15025213360786438},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W850948285","https://openalex.org/W1494765071","https://openalex.org/W1612936980","https://openalex.org/W1884191083","https://openalex.org/W1972101835","https://openalex.org/W1987869189","https://openalex.org/W1995642009","https://openalex.org/W2013503988","https://openalex.org/W2016811056","https://openalex.org/W2030984484","https://openalex.org/W2038600526","https://openalex.org/W2043300532","https://openalex.org/W2057318393","https://openalex.org/W2064570609","https://openalex.org/W2102860796","https://openalex.org/W2133059825","https://openalex.org/W2133467637","https://openalex.org/W2161711122","https://openalex.org/W2273520864","https://openalex.org/W2281060774","https://openalex.org/W2310992461","https://openalex.org/W2332066482","https://openalex.org/W2529926598","https://openalex.org/W2732931556","https://openalex.org/W3104258355","https://openalex.org/W6728528290"],"related_works":["https://openalex.org/W4385957115","https://openalex.org/W2061372042","https://openalex.org/W3047779762","https://openalex.org/W1520030019","https://openalex.org/W2071654592","https://openalex.org/W3001577138","https://openalex.org/W2240896738","https://openalex.org/W2998488741","https://openalex.org/W2993645418","https://openalex.org/W2790814253"],"abstract_inverted_index":{"A":[0],"quantum-inspired":[1],"self-supervised":[2,121],"neural":[3],"network":[4,49,122],"framework":[5,78],"titled":[6],"Quantum-Inspired":[7],"Optimized":[8],"Bi-Directional":[9],"Self-Organizing":[10],"Neural":[11],"Network":[12],"(Opti-QIBDS":[13],"Net)":[14],"suitable":[15],"for":[16],"fully":[17],"automated":[18],"MR":[19,130],"image":[20],"segmentation":[21],"is":[22,31,101],"suggested":[23,28],"in":[24,92],"this":[25],"article.":[26],"The":[27,48,69,118],"Opti-QIBDS":[29,53,76],"Net":[30,54,77],"characterized":[32],"by":[33,66],"Otsu's":[34],"multi-class":[35],"level":[36],"thresholding":[37],"scheme":[38],"based":[39,62],"optimized":[40,120],"Quantum":[41,99],"Inspired":[42],"Multi-level":[43],"Sigmoidal":[44],"(Opti-QIMUSIG)":[45],"activation":[46],"function.":[47],"layers":[50,73],"of":[51,74,84,115],"the":[52,75,105,109,113],"architecture":[55,123],"are":[56,79,90],"inter-connected":[57,80],"through":[58,81],"second":[59],"order":[60],"neighborhood":[61],"topology":[63],"and":[64,71,87,132,143],"constituted":[65],"quantum":[67,85,116],"neurons.":[68],"intermediate":[70],"output":[72],"counter":[82],"propagation":[83],"states":[86],"pixel":[88],"intensities":[89],"self-organized":[91],"counter-propagation":[93],"fashion":[94],"obviating":[95],"any":[96],"external":[97],"supervision.":[98],"observation":[100],"carried":[102],"out":[103],"at":[104],"end":[106],"to":[107,134],"obtain":[108],"segmented":[110],"tumor":[111],"from":[112],"superposition":[114],"states.":[117],"proposed":[119],"has":[124],"been":[125],"tested":[126],"on":[127],"T1":[128],"CE-weighted":[129],"images":[131],"found":[133],"be":[135],"very":[136],"efficient":[137],"while":[138],"compared":[139],"with":[140],"other":[141],"supervised":[142],"unsupervised":[144],"approaches.":[145]},"counts_by_year":[{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
