{"id":"https://openalex.org/W4411095667","doi":"https://doi.org/10.1145/3736717","title":"Topology Type Estimation of Simulated 4D Image Data by Combining Downscaling and Convolutional Neural Networks","display_name":"Topology Type Estimation of Simulated 4D Image Data by Combining Downscaling and Convolutional Neural Networks","publication_year":2025,"publication_date":"2025-06-06","ids":{"openalex":"https://openalex.org/W4411095667","doi":"https://doi.org/10.1145/3736717"},"language":"en","primary_location":{"id":"doi:10.1145/3736717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3736717","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3736717","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092346502","display_name":"Khalil Mathieu Hannouch","orcid":"https://orcid.org/0009-0008-3844-528X"},"institutions":[{"id":"https://openalex.org/I4210113003","display_name":"Physical Sciences (United States)","ror":"https://ror.org/021qvjc46","country_code":"US","type":"company","lineage":["https://openalex.org/I4210113003"]},{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU","US"],"is_corresponding":false,"raw_author_name":"Khalil Mathieu Hannouch","raw_affiliation_strings":["School of Information and Physical Sciences, The University of Newcastle","School of Information and Physical Sciences, The University of Newcastle, Callaghan, Australia"],"raw_orcid":"https://orcid.org/0009-0008-3844-528X","affiliations":[{"raw_affiliation_string":"School of Information and Physical Sciences, The University of Newcastle","institution_ids":["https://openalex.org/I4210113003","https://openalex.org/I78757542"]},{"raw_affiliation_string":"School of Information and Physical Sciences, The University of Newcastle, Callaghan, Australia","institution_ids":["https://openalex.org/I78757542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054614441","display_name":"Stephan K. Chalup","orcid":"https://orcid.org/0000-0002-7886-3653"},"institutions":[{"id":"https://openalex.org/I4210113003","display_name":"Physical Sciences (United States)","ror":"https://ror.org/021qvjc46","country_code":"US","type":"company","lineage":["https://openalex.org/I4210113003"]},{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU","US"],"is_corresponding":false,"raw_author_name":"Stephan Chalup","raw_affiliation_strings":["School of Information and Physical Sciences, The University of Newcastle","School of Information and Physical Sciences, The University of Newcastle, Callaghan, Australia"],"raw_orcid":"https://orcid.org/0000-0002-7886-3653","affiliations":[{"raw_affiliation_string":"School of Information and Physical Sciences, The University of Newcastle","institution_ids":["https://openalex.org/I4210113003","https://openalex.org/I78757542"]},{"raw_affiliation_string":"School of Information and Physical Sciences, The University of Newcastle, Callaghan, Australia","institution_ids":["https://openalex.org/I78757542"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5475,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63304163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"44","issue":"3","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9976000189781189,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9976000189781189,"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":0.9961000084877014,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9921000003814697,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7764747142791748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7071256637573242},{"id":"https://openalex.org/keywords/type","display_name":"Type (biology)","score":0.6315274834632874},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.5093244314193726},{"id":"https://openalex.org/keywords/downscaling","display_name":"Downscaling","score":0.5056202411651611},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5006258487701416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4944441318511963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46227461099624634},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44933032989501953},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3447178602218628},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21492722630500793},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.05681529641151428}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7764747142791748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7071256637573242},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.6315274834632874},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.5093244314193726},{"id":"https://openalex.org/C41156917","wikidata":"https://www.wikidata.org/wiki/Q682831","display_name":"Downscaling","level":3,"score":0.5056202411651611},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5006258487701416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4944441318511963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46227461099624634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44933032989501953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3447178602218628},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21492722630500793},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.05681529641151428},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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:10.1145/3736717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3736717","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3736717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3736717","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1135749636","display_name":null,"funder_award_id":"DP210103304","funder_id":"https://openalex.org/F4320315885","funder_display_name":"Australian Government"}],"funders":[{"id":"https://openalex.org/F4320315885","display_name":"Australian Government","ror":"https://ror.org/0314h5y94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W28435248","https://openalex.org/W107886322","https://openalex.org/W323266055","https://openalex.org/W1487997024","https://openalex.org/W1494599013","https://openalex.org/W1629010235","https://openalex.org/W1644641054","https://openalex.org/W1742512077","https://openalex.org/W1992605861","https://openalex.org/W2001141328","https://openalex.org/W2017522196","https://openalex.org/W2023353666","https://openalex.org/W2072112689","https://openalex.org/W2097117768","https://openalex.org/W2104339857","https://openalex.org/W2117539524","https://openalex.org/W2143983457","https://openalex.org/W2144044408","https://openalex.org/W2146019744","https://openalex.org/W2194775991","https://openalex.org/W2272065411","https://openalex.org/W2340140781","https://openalex.org/W2473748332","https://openalex.org/W2513541775","https://openalex.org/W2551276706","https://openalex.org/W2597608666","https://openalex.org/W2741292700","https://openalex.org/W2799305483","https://openalex.org/W2893477965","https://openalex.org/W2897540743","https://openalex.org/W2899011312","https://openalex.org/W2912764462","https://openalex.org/W2962731536","https://openalex.org/W2964209273","https://openalex.org/W2964237352","https://openalex.org/W2964342398","https://openalex.org/W2965485029","https://openalex.org/W2972175966","https://openalex.org/W2977755543","https://openalex.org/W3008606616","https://openalex.org/W3009750341","https://openalex.org/W3035965352","https://openalex.org/W3095568355","https://openalex.org/W3098455240","https://openalex.org/W3099878876","https://openalex.org/W3108032814","https://openalex.org/W3129083929","https://openalex.org/W3140579943","https://openalex.org/W3173434780","https://openalex.org/W3183719453","https://openalex.org/W3216692576","https://openalex.org/W4200493783","https://openalex.org/W4200577407","https://openalex.org/W4241215102","https://openalex.org/W4243494807","https://openalex.org/W4282013689","https://openalex.org/W4288447641","https://openalex.org/W4289306449","https://openalex.org/W4298067916","https://openalex.org/W4301223506","https://openalex.org/W4307138865","https://openalex.org/W4327495870","https://openalex.org/W4391306176","https://openalex.org/W4396677453","https://openalex.org/W4407474058","https://openalex.org/W6964771375"],"related_works":["https://openalex.org/W2394436593","https://openalex.org/W3013458534","https://openalex.org/W3010558748","https://openalex.org/W2526815458","https://openalex.org/W4220911053","https://openalex.org/W2380042710","https://openalex.org/W2944582722","https://openalex.org/W4377833746","https://openalex.org/W4387102043","https://openalex.org/W769766909"],"abstract_inverted_index":{"The":[0,64,153],"topological":[1,55],"analysis":[2],"of":[3,25,48,113,131,155],"four-dimensional":[4],"(4D)":[5],"image-type":[6,50],"data":[7,51,69,74,92],"is":[8,79,98],"challenged":[9],"by":[10,86,162],"the":[11,22,45,91,111,114,123,128,132,142,150,156,177],"immense":[12],"size":[13,58],"that":[14,53,77,106],"these":[15],"datasets":[16],"can":[17,20,108,125,158],"reach.":[18],"This":[19,40,97],"render":[21],"direct":[23],"application":[24],"methods,":[26],"like":[27],"persistent":[28,102,143],"homology":[29,103,112,144],"and":[30,57,70],"convolutional":[31],"neural":[32],"networks":[33],"(CNNs),":[34],"impractical":[35],"due":[36],"to":[37,43,81,90,168],"computational":[38,83],"constraints.":[39],"study":[41],"aims":[42],"estimate":[44,127],"topology":[46],"type":[47],"4D":[49,68],"cubes":[52,135],"exhibit":[54],"intricateness":[56],"above":[59],"our":[60],"current":[61],"processing":[62],"capacity.":[63],"experiments":[65],"using":[66],"synthesised":[67],"a":[71,95,165,169],"real-world":[72],"3D":[73],"set":[75],"demonstrate":[76],"it":[78],"possible":[80],"circumvent":[82],"complexity":[84],"issues":[85],"applying":[87],"downscaling":[88,107],"methods":[89],"before":[93],"training":[94,115,181],"CNN.":[96],"achievable":[99],"even":[100],"when":[101],"software":[104],"indicates":[105],"significantly":[109],"alter":[110],"data.":[116],"When":[117],"provided":[118],"with":[119,136],"downscaled":[120],"test":[121],"data,":[122],"CNN":[124],"still":[126],"Betti":[129,178],"numbers":[130,179],"original":[133],"sample":[134],"over":[137],"80%":[138],"accuracy,":[139],"which":[140],"outperforms":[141],"approach,":[145],"whose":[146],"accuracy":[147,154],"deteriorates":[148],"under":[149],"same":[151],"conditions.":[152],"CNNs":[157],"be":[159],"further":[160],"increased":[161],"moving":[163],"from":[164],"mathematically-guided":[166],"approach":[167,172],"more":[170],"vision-based":[171],"where":[173],"cavity":[174],"types":[175],"replace":[176],"as":[180],"targets.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-06-07T00:00:00"}
