{"id":"https://openalex.org/W2898021589","doi":"https://doi.org/10.1109/ijcnn.2018.8489193","title":"TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment","display_name":"TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2898021589","doi":"https://doi.org/10.1109/ijcnn.2018.8489193","mag":"2898021589"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5029055548","display_name":"Alexander Katzmann","orcid":"https://orcid.org/0000-0003-1958-1549"},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthcare (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Alexander Katzmann","raw_affiliation_strings":["Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany"],"affiliations":[{"raw_affiliation_string":"Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011400986","display_name":"Alexander M\u00fchlberg","orcid":"https://orcid.org/0000-0001-8039-844X"},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthcare (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Muhlberg","raw_affiliation_strings":["Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany"],"affiliations":[{"raw_affiliation_string":"Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064451296","display_name":"Michael S\u00fchling","orcid":"https://orcid.org/0000-0002-1060-3759"},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthcare (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Suhling","raw_affiliation_strings":["Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany"],"affiliations":[{"raw_affiliation_string":"Department CT R&D Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045575465","display_name":"Dominik N\u00f6renberg","orcid":null},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dominik Norenberg","raw_affiliation_strings":["Department of Radiology, University Hospital Gro\u00dfhadern, Ludwig-Maximilians-University Munich, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, University Hospital Gro\u00dfhadern, Ludwig-Maximilians-University Munich, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041927500","display_name":"Julian Walter Holch","orcid":"https://orcid.org/0000-0002-4755-0179"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Julian Walter Holch","raw_affiliation_strings":["Department of Internal Medicine III, University Hospital Gro\u00dfhadern, Ludwig-Maximilians-University Munich, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Internal Medicine III, University Hospital Gro\u00dfhadern, Ludwig-Maximilians-University Munich, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009771163","display_name":"and Horst-Michael Gros","orcid":null},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"and Horst-Michael Gros","raw_affiliation_strings":["Neuroinformatics and Cognitive Robotics Lab, University of Technology Ilmenau, Ilmenau, 98693, Germany"],"affiliations":[{"raw_affiliation_string":"Neuroinformatics and Cognitive Robotics Lab, University of Technology Ilmenau, Ilmenau, 98693, Germany","institution_ids":["https://openalex.org/I119449181"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5029055548"],"corresponding_institution_ids":["https://openalex.org/I4210153902"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17571437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":1.0,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":1.0,"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":0.9975000023841858,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9972000122070312,"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/autoencoder","display_name":"Autoencoder","score":0.6666281819343567},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5553909540176392},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5442133545875549},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5035218596458435},{"id":"https://openalex.org/keywords/response-evaluation-criteria-in-solid-tumors","display_name":"Response Evaluation Criteria in Solid Tumors","score":0.48849260807037354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4865635931491852},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4842798113822937},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.4529723525047302},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.43661078810691833},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.4256124794483185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32748639583587646},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.18240132927894592},{"id":"https://openalex.org/keywords/chemotherapy","display_name":"Chemotherapy","score":0.16148173809051514},{"id":"https://openalex.org/keywords/surgery","display_name":"Surgery","score":0.12000709772109985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11726966500282288}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6666281819343567},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5553909540176392},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5442133545875549},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5035218596458435},{"id":"https://openalex.org/C2779984678","wikidata":"https://www.wikidata.org/wiki/Q2145898","display_name":"Response Evaluation Criteria in Solid Tumors","level":4,"score":0.48849260807037354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4865635931491852},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4842798113822937},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.4529723525047302},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.43661078810691833},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.4256124794483185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32748639583587646},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.18240132927894592},{"id":"https://openalex.org/C2776694085","wikidata":"https://www.wikidata.org/wiki/Q974135","display_name":"Chemotherapy","level":2,"score":0.16148173809051514},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.12000709772109985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11726966500282288},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C2778822529","wikidata":"https://www.wikidata.org/wiki/Q1951525","display_name":"Progressive disease","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W855272188","https://openalex.org/W1836465849","https://openalex.org/W1858562615","https://openalex.org/W1884191083","https://openalex.org/W1901129140","https://openalex.org/W1904365287","https://openalex.org/W1945616565","https://openalex.org/W1970351649","https://openalex.org/W2019607817","https://openalex.org/W2089588713","https://openalex.org/W2097475056","https://openalex.org/W2099830092","https://openalex.org/W2103004421","https://openalex.org/W2128739912","https://openalex.org/W2155492044","https://openalex.org/W2174661749","https://openalex.org/W2225538867","https://openalex.org/W2226808373","https://openalex.org/W2409649574","https://openalex.org/W2432555109","https://openalex.org/W2470575266","https://openalex.org/W2586166317","https://openalex.org/W2621265919","https://openalex.org/W2746000686","https://openalex.org/W2903382683","https://openalex.org/W2963207607","https://openalex.org/W2963553763","https://openalex.org/W6602002561","https://openalex.org/W6623831209","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6640036494","https://openalex.org/W6640425456","https://openalex.org/W6689228519","https://openalex.org/W6738602561"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"In":[0],"tumor":[1,4,14,41,63,81,89,135],"therapy,":[2],"estimating":[3],"growth":[5,57],"is":[6,119],"crucial":[7],"to":[8,35,113,139,152],"get":[9],"an":[10,147],"early":[11],"information":[12],"regarding":[13],"therapy":[15],"response":[16,98,137],"and,":[17],"if":[18],"neccessary,":[19],"adapt":[20],"therapy.":[21],"We":[22],"propose":[23],"a":[24,37,52,71,127],"novel":[25],"deep":[26,31],"learning":[27],"based":[28,58],"algorithm":[29],"using":[30],"convolutional":[32],"sparse":[33],"autoencoders":[34],"find":[36],"minimal":[38],"representation":[39],"of":[40,54,77,131,149],"shape":[42],"and":[43,107],"texture":[44],"for":[45,73,84],"colorectal":[46],"liver":[47],"metastases.":[48],"Furthermore,":[49],"we":[50,125],"provide":[51],"prediction":[53],"future":[55],"lesion":[56],"on":[59],"single":[60,92],"slice":[61],"CT":[62,93],"images":[64],"which":[65],"prospectively":[66],"can":[67],"be":[68,114],"used":[69],"as":[70,95,144,146],"prognosis":[72],"physicians.":[74],"The":[75],"state":[76],"the":[78,96,103],"art":[79],"in":[80,91,133],"treatment":[82,97,109,136],"assessment":[83],"solid":[85],"tumors":[86],"mainly":[87],"uses":[88],"diameter":[90],"slices":[94],"criterion":[99],"(RECIST).":[100],"However,":[101],"whereas":[102],"correlation":[104,129],"between":[105],"RECIST":[106],"final":[108],"outcome":[110],"was":[111],"shown":[112],"significant,":[115],"its":[116],"effect":[117],"size":[118],"still":[120],"limited.":[121],"With":[122],"our":[123],"approach":[124],"achieve":[126],"Matthews":[128],"coefficient":[130],"52.0%":[132],"predicting":[134],"compared":[138],"28.2%":[140],"with":[141],"radiologic":[142],"assessment,":[143],"well":[145],"AUC":[148],"0.814":[150],"opposed":[151],"0.698.":[153]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
