StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder
Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segm...
Gespeichert in:
| Veröffentlicht in: | Computers in biology and medicine Jg. 149; S. 106093 |
|---|---|
| Hauptverfasser: | , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Elsevier Ltd
01.10.2022
Elsevier Limited |
| Schlagworte: | |
| ISSN: | 0010-4825, 1879-0534, 1879-0534 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI — even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies — lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the “context-encoding” VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively.
•Proposes unsupervised anomaly detection pipeline StRegA and compact ceVAE model.•The model is combined the proposed pre- and post-processing steps to form StRegA.•Trained on anomaly-free brain MRI datasets and evaluated for the task of brain tumour detection.•Proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset.•Achieved a Dice score of 0.859 ± 0.112 while detecting artificially induced anomalies. |
|---|---|
| AbstractList | Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI — even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies — lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the “context-encoding” VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively.
•Proposes unsupervised anomaly detection pipeline StRegA and compact ceVAE model.•The model is combined the proposed pre- and post-processing steps to form StRegA.•Trained on anomaly-free brain MRI datasets and evaluated for the task of brain tumour detection.•Proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset.•Achieved a Dice score of 0.859 ± 0.112 while detecting artificially induced anomalies. Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively.Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively. AbstractExpert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI — even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies — lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the “context-encoding” VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively. Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI — even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies — lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the “context-encoding” VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively. |
| ArticleNumber | 106093 |
| Author | Oeltze-Jafra, Steffen Sciarra, Alessandro Dünnwald, Max Speck, Oliver Nürnberger, Andreas Tummala, Pavan Chatterjee, Soumick Agrawal, Shubham Kumar Jauhari, Aishwarya Kalra, Aman |
| Author_xml | – sequence: 1 givenname: Soumick orcidid: 0000-0001-7594-1188 surname: Chatterjee fullname: Chatterjee, Soumick email: soumick.chatterjee@ovgu.de, contact@soumick.com organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 2 givenname: Alessandro surname: Sciarra fullname: Sciarra, Alessandro organization: Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany – sequence: 3 givenname: Max surname: Dünnwald fullname: Dünnwald, Max organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 4 givenname: Pavan surname: Tummala fullname: Tummala, Pavan organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 5 givenname: Shubham Kumar surname: Agrawal fullname: Agrawal, Shubham Kumar organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 6 givenname: Aishwarya surname: Jauhari fullname: Jauhari, Aishwarya organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 7 givenname: Aman surname: Kalra fullname: Kalra, Aman organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany – sequence: 8 givenname: Steffen surname: Oeltze-Jafra fullname: Oeltze-Jafra, Steffen organization: MedDigit, Department of Neurology, Medical Faculty, University Hospital, Magdeburg, Germany – sequence: 9 givenname: Oliver surname: Speck fullname: Speck, Oliver organization: Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany – sequence: 10 givenname: Andreas surname: Nürnberger fullname: Nürnberger, Andreas organization: Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany |
| BookMark | eNqNUt9rFDEQDlLB6-n_EPDFlz0nyd7-8KFYi62FitDa55Bk50rO3eRMsof335vtisKBcC-ZkPnmy8z3zTk5c94hIZTBigGr3m9Xxg87bf2A3YoD5_m5gla8IAvW1G0Ba1GekQUAg6Js-PoVOY9xCwAlCFiQ4SHd49PlB_ro4rjDsLcRO6qcH1R_oB0mNMl6R62jOqh8fr2_jXSM1j1RRaevlUk5uoS_UoHO-G5K7VWwaipUPVVj8s8JDK_Jy43qI775E5fk8frz96svxd23m9ury7vClC1LRaOh41hvlBBlXekW-VrpDWcGtS6Bdxp01TZtA0bU0GJVshaU0sAEA9QNiiV5N_Pugv85YkxysNFg3yuHfoyS12xdt6LNOi3J2yPo1o8h9z2juFhXZZlRFzPKBB9jwI00Nj0PmLIqvWQgJzfkVv5zQ05uyNmNTNAcEeyCHVQ4nFL6aS7FrNjeYpDR2CwodjZkd2Tn7SkkF0ckprfOGtX_wAPGvzMzGbkE-TDty7QunAM0-Z4JPv6f4LQefgOX6NiH |
| CitedBy_id | crossref_primary_10_1016_j_compmedimag_2024_102372 crossref_primary_10_1016_j_nucengdes_2023_112712 crossref_primary_10_21833_ijaas_2025_08_024 crossref_primary_10_1016_j_bspc_2024_107063 crossref_primary_10_1007_s10489_024_05647_z crossref_primary_10_1515_mr_2024_0086 crossref_primary_10_1016_j_ascom_2024_100921 crossref_primary_10_1177_14759217251377641 crossref_primary_10_1016_j_media_2025_103623 crossref_primary_10_1016_j_neucom_2024_127761 crossref_primary_10_1007_s11831_025_10238_3 crossref_primary_10_3390_diagnostics13111925 crossref_primary_10_3390_healthcare13151776 crossref_primary_10_1016_j_asoc_2024_111919 crossref_primary_10_1109_TAI_2023_3323918 |
| Cites_doi | 10.1016/j.media.2022.102475 10.1016/j.enbuild.2019.109689 10.1016/j.cviu.2020.102920 10.1016/j.patrec.2017.07.016 10.1016/j.nicl.2017.12.022 10.1007/s13244-016-0534-1 10.1016/j.compbiomed.2022.105620 10.1016/j.compbiomed.2022.105810 10.1016/S1474-4422(13)70124-8 10.1016/j.neuroimage.2011.09.015 10.1016/j.eswa.2011.09.088 10.1016/j.cmpb.2020.105639 10.1145/3178876.3185996 10.1177/1352458517751647 10.1109/TMI.2014.2377694 10.1148/rg.2015150023 10.1145/3466826.3466843 10.1109/42.811270 10.1002/aic.690370209 10.1016/j.sigpro.2013.12.026 10.1016/j.neucom.2019.08.044 10.1145/1007730.1007738 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd Elsevier Ltd 2022. Elsevier Ltd Copyright © 2022 Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2022 Elsevier Ltd – notice: Elsevier Ltd – notice: 2022. Elsevier Ltd – notice: Copyright © 2022 Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION 3V. 7RV 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK 8G5 ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ GUQSH HCIFZ JQ2 K7- K9. KB0 LK8 M0N M0S M1P M2O M7P M7Z MBDVC NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 |
| DOI | 10.1016/j.compbiomed.2022.106093 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Biological Sciences Computing Database ProQuest Health & Medical Collection Medical Database Research Library ProQuest Biological Science Biochemistry Abstracts 1 Research Library (Corporate) Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic |
| DatabaseTitle | CrossRef Research Library Prep Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Research Library ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Biochemistry Abstracts 1 ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Research Library Prep |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1879-0534 |
| EndPage | 106093 |
| ExternalDocumentID | 10_1016_j_compbiomed_2022_106093 S0010482522008010 1_s2_0_S0010482522008010 |
| GroupedDBID | --- --K --M --Z -~X .1- .55 .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5VS 7-5 71M 77I 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8G5 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABOCM ABUWG ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACIWK ACLOT ACNNM ACPRK ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFKRA AFPUW AFRAH AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AHMBA AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BGLVJ BHPHI BKEYQ BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DU5 DWQXO EBS EFJIC EFKBS EFLBG EJD EMOBN EO8 EO9 EP2 EP3 EX3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GBOLZ GNUQQ GUQSH HCIFZ HLZ HMCUK HMK HMO HVGLF HZ~ IHE J1W K6V K7- KOM LK8 LX9 M1P M29 M2O M41 M7P MO0 N9A NAPCQ O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO Q38 R2- ROL RPZ RXW SAE SBC SCC SDF SDG SDP SEL SES SEW SPC SPCBC SSH SSV SSZ SV3 T5K TAE UAP UKHRP WOW WUQ X7M XPP Z5R ZGI ~G- ~HD 3V. AACTN AFCTW AFKWA AJOXV ALIPV AMFUW M0N RIG AAIAV ABLVK ABYKQ AHPSJ AJBFU LCYCR 9DU AAYXX AFFHD CITATION 7XB 8AL 8FD 8FK FR3 JQ2 K9. M7Z MBDVC P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO |
| ID | FETCH-LOGICAL-c491t-8b0d2e7fa33476b9e25abf21cebb402db0b698980c3709e64190aab01310eb8e3 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000861361700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0010-4825 1879-0534 |
| IngestDate | Sun Sep 28 05:50:15 EDT 2025 Sat Nov 29 14:39:20 EST 2025 Sat Nov 29 07:31:14 EST 2025 Tue Nov 18 22:31:38 EST 2025 Fri Feb 23 02:40:07 EST 2024 Tue Feb 25 20:12:01 EST 2025 Tue Oct 14 19:33:19 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Brain tumour segmentation MRI Anomaly detection Unsupervised learning |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c491t-8b0d2e7fa33476b9e25abf21cebb402db0b698980c3709e64190aab01310eb8e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-7594-1188 |
| PQID | 2715235644 |
| PQPubID | 1226355 |
| PageCount | 1 |
| ParticipantIDs | proquest_miscellaneous_2715793909 proquest_journals_2715235644 crossref_citationtrail_10_1016_j_compbiomed_2022_106093 crossref_primary_10_1016_j_compbiomed_2022_106093 elsevier_sciencedirect_doi_10_1016_j_compbiomed_2022_106093 elsevier_clinicalkeyesjournals_1_s2_0_S0010482522008010 elsevier_clinicalkey_doi_10_1016_j_compbiomed_2022_106093 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-10-01 |
| PublicationDateYYYYMMDD | 2022-10-01 |
| PublicationDate_xml | – month: 10 year: 2022 text: 2022-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Computers in biology and medicine |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd Elsevier Limited |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier Limited |
| References | Baur, Wiestler, Albarqouni, Navab (b32) 2020 Brady (b2) 2017; 8 Kingma, Ba (b45) 2014 Qi, Zhao, Yu, Heidari, Wu, Cai, Alenezi, Mansour, Chen, Chen (b47) 2022; 148 Van Leemput, Maes, Vandermeulen, Suetens (b42) 1999; 18 An, Cho (b33) 2015; 2 Iuso, Chatterjee, Heylen, Cornelissen, De Beenhouwer, Sijbers (b48) 2022; 12242 Gul, Khan, Bibi, Khandakar, Ayari, Chowdhury (b46) 2022 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b19) 2014; 27 Kramer (b26) 1991; 37 Makhzani, Shlens, Jaitly, Goodfellow, Frey (b28) 2016 Fan, Wen, Li, Qiu, Levine, Xiao (b39) 2020; 195 Guo, Liao, Wang, Yu, Ji, Li (b38) 2018 Pinaya, Tudosiu, Gray, Rees, Nachev, Ourselin, Cardoso (b16) 2022; 79 Zimmerer, Isensee, Petersen, Kohl, Maier-Hein (b13) 2019 Li, Chang (b30) 2019; 369 García González, Casas, Fernández, Gómez (b15) 2021; 48 Kim, Kang, Cho, Lee, Doh (b11) 2012; 39 Hagens, Burggraaff, Kilsdonk, Ruggieri, Collorone, Cortese, Cawley, Sbardella, Andelova, Amann (b1) 2019; 25 Pimentel, Clifton, Clifton, Tarassenko (b7) 2014; 99 Liu, Zhao, Chen, Yu, She (b14) 2020; 196 Kingma, Welling (b20) 2013 Ng (b27) 2011; 72 Beggel, Pfeiffer, Bischl (b29) 2019 Akcay, Atapour-Abarghouei, Breckon (b10) 2018 Menze, Jakab, Bauer, Kalpathy-Cramer, Farahani, Kirby, Burren, Porz, Slotboom, Wiest (b4) 2014; 34 Liu, Xu, Li, Zhang, Li (b12) 2021 Bruno, Walker, Abujudeh (b3) 2015; 35 Guerrero, Qin, Oktay, Bowles, Chen, Joules, Wolz, Valdés-Hernández, Dickie, Wardlaw (b5) 2018; 17 H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, 2018, pp. 187–196. Carrara, Amato, Brombin, Falchi, Gennaro (b23) 2021 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (b24) 2017; 30 Chatterjee, Sciarra, Dünnwald, Agrawal, Tummala, Setlur, Kalra, Jauhari, Oeltze-Jafra, Speck, Nürnberger (b44) 2021 Dilokthanakul, Mediano, Garnelo, Lee, Salimbeni, Arulkumaran, Shanahan (b36) 2016 Baur, Wiestler, Albarqouni, Navab (b31) 2020 Oord, Vinyals, Kavukcuoglu (b34) 2017 Yan, Huang, Shen, Ji (b22) 2020; 210 Alain, Bengio (b17) 2014; 15 Phua, Alahakoon, Lee (b8) 2004; 6 Clifton, Bannister, Tarassenko (b9) 2007 Jenkinson, Beckmann, Behrens, Woolrich, Smith (b41) 2012; 62 Ribeiro, Lazzaretti, Lopes (b18) 2018; 105 Rumelhart, Hinton, Williams (b25) 1985 Marimont, Tarroni (b35) 2020 Chen, You, Tezcan, Konukoglu (b37) 2020 Wardlaw, Smith, Biessels, Cordonnier, Fazekas, Frayne, Lindley, T O’Brien, Barkhof, Benavente (b6) 2013; 12 Zimmerer, Kohl, Petersen, Isensee, Maier-Hein (b40) 2018 Zimmerer, Petersen, Köhler, Jäger, Full, Roß, Adler, Reinke, Maier-Hein, Maier-Hein (b43) 2021 Wardlaw (10.1016/j.compbiomed.2022.106093_b6) 2013; 12 10.1016/j.compbiomed.2022.106093_b21 Makhzani (10.1016/j.compbiomed.2022.106093_b28) 2016 Ng (10.1016/j.compbiomed.2022.106093_b27) 2011; 72 Van Leemput (10.1016/j.compbiomed.2022.106093_b42) 1999; 18 Pimentel (10.1016/j.compbiomed.2022.106093_b7) 2014; 99 Li (10.1016/j.compbiomed.2022.106093_b30) 2019; 369 Qi (10.1016/j.compbiomed.2022.106093_b47) 2022; 148 Zimmerer (10.1016/j.compbiomed.2022.106093_b13) 2019 Vaswani (10.1016/j.compbiomed.2022.106093_b24) 2017; 30 Beggel (10.1016/j.compbiomed.2022.106093_b29) 2019 Baur (10.1016/j.compbiomed.2022.106093_b31) 2020 Bruno (10.1016/j.compbiomed.2022.106093_b3) 2015; 35 Iuso (10.1016/j.compbiomed.2022.106093_b48) 2022; 12242 Alain (10.1016/j.compbiomed.2022.106093_b17) 2014; 15 Kramer (10.1016/j.compbiomed.2022.106093_b26) 1991; 37 Dilokthanakul (10.1016/j.compbiomed.2022.106093_b36) 2016 Guerrero (10.1016/j.compbiomed.2022.106093_b5) 2018; 17 Hagens (10.1016/j.compbiomed.2022.106093_b1) 2019; 25 Menze (10.1016/j.compbiomed.2022.106093_b4) 2014; 34 Liu (10.1016/j.compbiomed.2022.106093_b14) 2020; 196 Guo (10.1016/j.compbiomed.2022.106093_b38) 2018 Brady (10.1016/j.compbiomed.2022.106093_b2) 2017; 8 Phua (10.1016/j.compbiomed.2022.106093_b8) 2004; 6 Goodfellow (10.1016/j.compbiomed.2022.106093_b19) 2014; 27 García González (10.1016/j.compbiomed.2022.106093_b15) 2021; 48 Pinaya (10.1016/j.compbiomed.2022.106093_b16) 2022; 79 Oord (10.1016/j.compbiomed.2022.106093_b34) 2017 An (10.1016/j.compbiomed.2022.106093_b33) 2015; 2 Kim (10.1016/j.compbiomed.2022.106093_b11) 2012; 39 Jenkinson (10.1016/j.compbiomed.2022.106093_b41) 2012; 62 Carrara (10.1016/j.compbiomed.2022.106093_b23) 2021 Rumelhart (10.1016/j.compbiomed.2022.106093_b25) 1985 Chen (10.1016/j.compbiomed.2022.106093_b37) 2020 Baur (10.1016/j.compbiomed.2022.106093_b32) 2020 Liu (10.1016/j.compbiomed.2022.106093_b12) 2021 Yan (10.1016/j.compbiomed.2022.106093_b22) 2020; 210 Marimont (10.1016/j.compbiomed.2022.106093_b35) 2020 Fan (10.1016/j.compbiomed.2022.106093_b39) 2020; 195 Zimmerer (10.1016/j.compbiomed.2022.106093_b40) 2018 Gul (10.1016/j.compbiomed.2022.106093_b46) 2022 Akcay (10.1016/j.compbiomed.2022.106093_b10) 2018 Clifton (10.1016/j.compbiomed.2022.106093_b9) 2007 Ribeiro (10.1016/j.compbiomed.2022.106093_b18) 2018; 105 Zimmerer (10.1016/j.compbiomed.2022.106093_b43) 2021 Kingma (10.1016/j.compbiomed.2022.106093_b45) 2014 Chatterjee (10.1016/j.compbiomed.2022.106093_b44) 2021 Kingma (10.1016/j.compbiomed.2022.106093_b20) 2013 |
| References_xml | – year: 2020 ident: b37 article-title: Unsupervised lesion detection via image restoration with a normative prior – volume: 62 start-page: 782 year: 2012 end-page: 790 ident: b41 article-title: Fsl publication-title: Neuroimage – volume: 12 start-page: 822 year: 2013 end-page: 838 ident: b6 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. – year: 2020 ident: b31 article-title: Scale-space autoencoders for unsupervised anomaly segmentation in brain MRI – volume: 35 start-page: 1668 year: 2015 end-page: 1676 ident: b3 article-title: Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction publication-title: Radiographics – volume: 37 start-page: 233 year: 1991 end-page: 243 ident: b26 article-title: Nonlinear principal component analysis using autoassociative neural networks publication-title: AIChE J. – start-page: 2399 year: 2021 ident: b44 article-title: Unsupervised reconstruction based anomaly detection using a variational auto encoder publication-title: 2021 ISMRM & SMRT Annual Meeting & Exhibition – year: 1985 ident: b25 article-title: Learning internal representations by error propagation – year: 2020 ident: b35 article-title: Anomaly detection through latent space restoration using vector-quantized variational autoencoders – year: 2016 ident: b28 article-title: Adversarial autoencoders – start-page: 97 year: 2018 end-page: 112 ident: b38 article-title: Multidimensional time series anomaly detection: A gru-based gaussian mixture variational autoencoder approach publication-title: Asian Conference on Machine Learning – volume: 39 start-page: 4075 year: 2012 end-page: 4083 ident: b11 article-title: Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing publication-title: Expert Syst. Appl. – volume: 18 start-page: 897 year: 1999 end-page: 908 ident: b42 article-title: Automated model-based tissue classification of MR images of the brain publication-title: IEEE Trans. Med. Imaging – year: 2021 ident: b12 article-title: Anomaly detection with representative neighbors publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 196 year: 2020 ident: b14 article-title: Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records publication-title: Comput. Methods Programs Biomed. – start-page: 3939 year: 2021 end-page: 3946 ident: b23 article-title: Combining gans and autoencoders for efficient anomaly detection publication-title: 2020 25th International Conference on Pattern Recognition (ICPR) – volume: 369 start-page: 92 year: 2019 end-page: 105 ident: b30 article-title: Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder publication-title: Neurocomputing – volume: 2 start-page: 1 year: 2015 end-page: 18 ident: b33 article-title: Variational autoencoder based anomaly detection using reconstruction probability publication-title: Spec. Lect. IE – volume: 30 year: 2017 ident: b24 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – volume: 17 start-page: 918 year: 2018 end-page: 934 ident: b5 article-title: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks publication-title: NeuroImage: Clinical – volume: 12242 year: 2022 ident: b48 article-title: Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing publication-title: Developments in X-Ray Tomography XIV – volume: 25 start-page: 352 year: 2019 end-page: 360 ident: b1 article-title: Impact of 3 tesla MRI on interobserver agreement in clinically isolated syndrome: a MAGNIMS multicentre study publication-title: Multiple Scler. J. – volume: 6 start-page: 50 year: 2004 end-page: 59 ident: b8 article-title: Minority report in fraud detection: classification of skewed data publication-title: ACM SIGKDD Explor. Newsl. – year: 2019 ident: b13 article-title: Unsupervised anomaly localization using variational auto-encoders – volume: 27 year: 2014 ident: b19 article-title: Generative adversarial nets publication-title: Adv. Neural Inf. Process. Syst. – year: 2016 ident: b36 article-title: Deep unsupervised clustering with Gaussian mixture variational autoencoders – year: 2017 ident: b34 article-title: Neural discrete representation learning – start-page: 1905 year: 2020 end-page: 1909 ident: b32 article-title: Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain mri publication-title: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) – volume: 48 start-page: 49 year: 2021 end-page: 52 ident: b15 article-title: On the usage of generative models for network anomaly detection in multivariate time-series publication-title: ACM SIGMETRICS Perform. Eval. Rev. – year: 2013 ident: b20 article-title: Auto-encoding variational bayes – year: 2019 ident: b29 article-title: Robust anomaly detection in images using adversarial autoencoders – volume: 210 year: 2020 ident: b22 article-title: Unsupervised learning for fault detection and diagnosis of air handling units publication-title: Energy Build. – volume: 79 year: 2022 ident: b16 article-title: Unsupervised brain imaging 3D anomaly detection and segmentation with transformers publication-title: Med. Image Anal. – volume: 105 start-page: 13 year: 2018 end-page: 22 ident: b18 article-title: A study of deep convolutional auto-encoders for anomaly detection in videos publication-title: Pattern Recognit. Lett. – volume: 72 start-page: 1 year: 2011 end-page: 19 ident: b27 article-title: Sparse autoencoder publication-title: CS294A Lect. Not. – volume: 34 start-page: 1993 year: 2014 end-page: 2024 ident: b4 article-title: The multimodal brain tumor image segmentation benchmark (BRATS) publication-title: IEEE Trans. Med. Imaging – year: 2021 ident: b43 article-title: Medical out-of-distribution analysis challenge 2021 – year: 2022 ident: b46 article-title: Deep learning techniques for liver and liver tumor segmentation: A review publication-title: Comput. Biol. Med. – volume: 99 start-page: 215 year: 2014 end-page: 249 ident: b7 article-title: A review of novelty detection publication-title: Signal Process. – volume: 8 start-page: 171 year: 2017 end-page: 182 ident: b2 article-title: Error and discrepancy in radiology: inevitable or avoidable? publication-title: Insights Imaging – volume: 195 year: 2020 ident: b39 article-title: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder publication-title: Comput. Vis. Image Underst. – volume: 148 year: 2022 ident: b47 article-title: Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation publication-title: Comput. Biol. Med. – volume: 15 start-page: 3563 year: 2014 end-page: 3593 ident: b17 article-title: What regularized auto-encoders learn from the data-generating distribution publication-title: J. Mach. Learn. Res. – reference: H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, 2018, pp. 187–196. – year: 2014 ident: b45 article-title: Adam: A method for stochastic optimization – year: 2018 ident: b40 article-title: Context-encoding variational autoencoder for unsupervised anomaly detection – start-page: 305 year: 2007 end-page: 310 ident: b9 article-title: A framework for novelty detection in jet engine vibration data publication-title: Key Engineering Materials, Vol. 347 – start-page: 622 year: 2018 end-page: 637 ident: b10 article-title: Ganomaly: Semi-supervised anomaly detection via adversarial training publication-title: Asian Conference on Computer Vision – volume: 72 start-page: 1 issue: 2011 year: 2011 ident: 10.1016/j.compbiomed.2022.106093_b27 article-title: Sparse autoencoder publication-title: CS294A Lect. Not. – year: 1985 ident: 10.1016/j.compbiomed.2022.106093_b25 – start-page: 622 year: 2018 ident: 10.1016/j.compbiomed.2022.106093_b10 article-title: Ganomaly: Semi-supervised anomaly detection via adversarial training – volume: 79 year: 2022 ident: 10.1016/j.compbiomed.2022.106093_b16 article-title: Unsupervised brain imaging 3D anomaly detection and segmentation with transformers publication-title: Med. Image Anal. doi: 10.1016/j.media.2022.102475 – volume: 210 year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b22 article-title: Unsupervised learning for fault detection and diagnosis of air handling units publication-title: Energy Build. doi: 10.1016/j.enbuild.2019.109689 – volume: 195 year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b39 article-title: Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2020.102920 – volume: 105 start-page: 13 year: 2018 ident: 10.1016/j.compbiomed.2022.106093_b18 article-title: A study of deep convolutional auto-encoders for anomaly detection in videos publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2017.07.016 – year: 2014 ident: 10.1016/j.compbiomed.2022.106093_b45 – volume: 15 start-page: 3563 issue: 1 year: 2014 ident: 10.1016/j.compbiomed.2022.106093_b17 article-title: What regularized auto-encoders learn from the data-generating distribution publication-title: J. Mach. Learn. Res. – year: 2021 ident: 10.1016/j.compbiomed.2022.106093_b43 – volume: 17 start-page: 918 year: 2018 ident: 10.1016/j.compbiomed.2022.106093_b5 article-title: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks publication-title: NeuroImage: Clinical doi: 10.1016/j.nicl.2017.12.022 – year: 2021 ident: 10.1016/j.compbiomed.2022.106093_b12 article-title: Anomaly detection with representative neighbors publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 8 start-page: 171 issue: 1 year: 2017 ident: 10.1016/j.compbiomed.2022.106093_b2 article-title: Error and discrepancy in radiology: inevitable or avoidable? publication-title: Insights Imaging doi: 10.1007/s13244-016-0534-1 – year: 2022 ident: 10.1016/j.compbiomed.2022.106093_b46 article-title: Deep learning techniques for liver and liver tumor segmentation: A review publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105620 – volume: 148 year: 2022 ident: 10.1016/j.compbiomed.2022.106093_b47 article-title: Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105810 – volume: 27 year: 2014 ident: 10.1016/j.compbiomed.2022.106093_b19 article-title: Generative adversarial nets publication-title: Adv. Neural Inf. Process. Syst. – year: 2017 ident: 10.1016/j.compbiomed.2022.106093_b34 – year: 2018 ident: 10.1016/j.compbiomed.2022.106093_b40 – volume: 12 start-page: 822 issue: 8 year: 2013 ident: 10.1016/j.compbiomed.2022.106093_b6 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(13)70124-8 – year: 2013 ident: 10.1016/j.compbiomed.2022.106093_b20 – year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b31 – volume: 62 start-page: 782 issue: 2 year: 2012 ident: 10.1016/j.compbiomed.2022.106093_b41 article-title: Fsl publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.09.015 – volume: 39 start-page: 4075 issue: 4 year: 2012 ident: 10.1016/j.compbiomed.2022.106093_b11 article-title: Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.09.088 – volume: 196 year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b14 article-title: Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105639 – year: 2016 ident: 10.1016/j.compbiomed.2022.106093_b36 – ident: 10.1016/j.compbiomed.2022.106093_b21 doi: 10.1145/3178876.3185996 – volume: 25 start-page: 352 issue: 3 year: 2019 ident: 10.1016/j.compbiomed.2022.106093_b1 article-title: Impact of 3 tesla MRI on interobserver agreement in clinically isolated syndrome: a MAGNIMS multicentre study publication-title: Multiple Scler. J. doi: 10.1177/1352458517751647 – volume: 30 year: 2017 ident: 10.1016/j.compbiomed.2022.106093_b24 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – volume: 34 start-page: 1993 issue: 10 year: 2014 ident: 10.1016/j.compbiomed.2022.106093_b4 article-title: The multimodal brain tumor image segmentation benchmark (BRATS) publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – volume: 35 start-page: 1668 issue: 6 year: 2015 ident: 10.1016/j.compbiomed.2022.106093_b3 article-title: Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction publication-title: Radiographics doi: 10.1148/rg.2015150023 – volume: 12242 year: 2022 ident: 10.1016/j.compbiomed.2022.106093_b48 article-title: Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing – start-page: 1905 year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b32 article-title: Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain mri – start-page: 305 year: 2007 ident: 10.1016/j.compbiomed.2022.106093_b9 article-title: A framework for novelty detection in jet engine vibration data – volume: 48 start-page: 49 issue: 4 year: 2021 ident: 10.1016/j.compbiomed.2022.106093_b15 article-title: On the usage of generative models for network anomaly detection in multivariate time-series publication-title: ACM SIGMETRICS Perform. Eval. Rev. doi: 10.1145/3466826.3466843 – volume: 18 start-page: 897 issue: 10 year: 1999 ident: 10.1016/j.compbiomed.2022.106093_b42 article-title: Automated model-based tissue classification of MR images of the brain publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.811270 – volume: 37 start-page: 233 issue: 2 year: 1991 ident: 10.1016/j.compbiomed.2022.106093_b26 article-title: Nonlinear principal component analysis using autoassociative neural networks publication-title: AIChE J. doi: 10.1002/aic.690370209 – volume: 99 start-page: 215 year: 2014 ident: 10.1016/j.compbiomed.2022.106093_b7 article-title: A review of novelty detection publication-title: Signal Process. doi: 10.1016/j.sigpro.2013.12.026 – year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b35 – volume: 369 start-page: 92 year: 2019 ident: 10.1016/j.compbiomed.2022.106093_b30 article-title: Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.08.044 – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.compbiomed.2022.106093_b33 article-title: Variational autoencoder based anomaly detection using reconstruction probability publication-title: Spec. Lect. IE – volume: 6 start-page: 50 issue: 1 year: 2004 ident: 10.1016/j.compbiomed.2022.106093_b8 article-title: Minority report in fraud detection: classification of skewed data publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1007730.1007738 – start-page: 2399 year: 2021 ident: 10.1016/j.compbiomed.2022.106093_b44 article-title: Unsupervised reconstruction based anomaly detection using a variational auto encoder – start-page: 3939 year: 2021 ident: 10.1016/j.compbiomed.2022.106093_b23 article-title: Combining gans and autoencoders for efficient anomaly detection – year: 2020 ident: 10.1016/j.compbiomed.2022.106093_b37 – year: 2019 ident: 10.1016/j.compbiomed.2022.106093_b13 – year: 2019 ident: 10.1016/j.compbiomed.2022.106093_b29 – year: 2016 ident: 10.1016/j.compbiomed.2022.106093_b28 – start-page: 97 year: 2018 ident: 10.1016/j.compbiomed.2022.106093_b38 article-title: Multidimensional time series anomaly detection: A gru-based gaussian mixture variational autoencoder approach |
| SSID | ssj0004030 |
| Score | 2.4364386 |
| Snippet | Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been... AbstractExpert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 106093 |
| SubjectTerms | Abnormalities Annotations Anomalies Anomaly detection Brain Brain cancer Brain tumors Brain tumour segmentation Context Datasets Image segmentation Internal Medicine Machine learning Medical imaging MRI Neuroimaging Other Training Tumors Unsupervised learning |
| Title | StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0010482522008010 https://www.clinicalkey.es/playcontent/1-s2.0-S0010482522008010 https://dx.doi.org/10.1016/j.compbiomed.2022.106093 https://www.proquest.com/docview/2715235644 https://www.proquest.com/docview/2715793909 |
| Volume | 149 |
| WOSCitedRecordID | wos000861361700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0534 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: P5Z dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: M7P dateStart: 20030101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: K7- dateStart: 20030101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: 7X7 dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: 7RV dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: BENPR dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 1879-0534 dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: M2O dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELfYhhAvfCMKozISrxGO8-EYHtBAm0CopeoYqnix7NiZhrakNOkk_nvuHKeVEKBKvFhpk6sj3fV3d_bPd4S81EYYk7MyqgorolTkOipMnoEtQzDrrIwBl32zCTGdFouFnIUFtzbQKgdM9EBtmxLXyF9xAZ4mycB9v13-iLBrFO6uhhYae-QAqyQknro3256LZEl_BAWwJoVUKDB5en4XUrb7I-6QJXIOX-dMJn9zT78Btfc-J3f_973vkTsh7qRHvaHcJzdc_YDcmoSd9Yfk6rSbu_Oj1_SsbtdLBJDWWarr5kpf_qTWdZ6yVdOLmhrsKkEn848tRdb8OdXUM9nLjiLxHVNprI6JTpFeQyoelhupXneNv-FWj8jZyfGX9x-i0IohKlMZd6A_ZrkTlU4S0KeRjmfaVDwunTGQgVrDjO9EycpEMOnyFOIMrXGNNWbOFC55TPbrpnZPCLVxVulcJzkzNnXWGJlWpYQHK2tTbu2IiEEDqgx1yrFdxqUaCGnf1VZ3CnWnet2NSLyRXPa1OnaQkYOS1XAWFdBTgUPZQVb8Sda1AQZaFauWK6ZOfRUkMECObBO4HpE3G8kQ6fQRzI7zHg6mpjZTbe1sRF5sbgNW4AaQrl2z7p8BPJZMPv33Tzwjt3G-nrR4SPa71do9JzfL6-6iXY3Jnph_xXEh_FiMycG74-lsDp8-iQjGCf889n9FGGfZt1-Xxjn- |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aAwEv3BGFAUaCxwjHSeMahNAETKu2Vqjb0N6Mb5mKtqQ06dD-FL-R4zhpJQSoL3vgLYpz7Fw-n892vuMD8FJprnVGTZQPLI9SnqlooLM-YhkHs86KGP1yk2yCj8eD42PxeQN-drEwXlbZ-cTGUdvS-DXy14wj0yR9pO_3s--Rzxrl_652KTQCLPbcxQ-cslXvhh_x-75ibOfT4YfdqM0qEJlUxDXeCrXM8VwlCd6aFo71lc5ZbJzWOJmymuomqSI1CafCZSlSplJ-uTCmTg9cgvVegavox2MvIeOTL6s4TJqEkBf0bSlOvVrlUNCTeYl4CKnHWSljeDqjIvkbHf5GDA3b7dz-397THbjVjqvJdugId2HDFffg-qhVDtyHs4N64k6235CjolrMvIOsnCWqKM_U6QWxrm4kaQWZFkT7rBlkNBlWxEcFnBBFGqW-qYkX9vulAr_7pyd9cq7m03Y5lahFXTYFbv4Aji7lYR_CZlEW7hEQG_dzlakko9qmzmot0twIvDC3NmXW9oB3X1yadh92nw7kVHaCu29yhRXpsSIDVnoQLy1nYS-SNWxEByrZxdoiO0gkzDVs-Z9sXdW6uUrGsmKSyoNmlycEPPNqGjzuwdulZTuSCyO0Ndvd6qAtl02tcN2DF8ti9IX-B5cqXLkI1yDfCCoe_7uK53Bj93C0L_eH470ncNO3HQSaW7BZzxfuKVwz5_W0mj9rOjiBr5fdU34BbAeP2Q |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fb9MwED6NgSZe-D1R2MBI8BjNcX64BqFpYlRUZdW0MWlvxo6dqWhLSpMO7V_bX7dznLQSAtSXPfBWNbk4bb677-x85wN4qzTXOqVZkPcND2KeqqCv0wSxjMmsNSLEuNw0m-Djcf_0VByuwXVXC-NklV1MbAK1KTO3Rr7DODJNlCB97-StLOJwf7A7_Rm4DlLuTWvXTsNDZGSvfuH0rfo43Mdn_Y6xwedvn74EbYeBIItFWONtUcMsz1UU4W1qYVmidM7CzGqNEyujqW4aLNIs4lTYNEb6VMotHYbU6r6N8Lp34C6ycOJ8bMSDZU0mjXz5C8a5GKdhrYrIa8ucXNyX1-MMlTH8OqUi-hs1_kYSDfMNHv7P_9kjeNDm22TPO8hjWLPFE9g4aBUFT-HiuD6yZ3vvyUlRzacucFbWEFWUF-r8ihhbN1K1gkwKol03DXJwNKyIqxY4I4o0Cv6sJk7w75YQ3K6gLhkgl2o2aZdZiZrXZXPAzp7Bya382E1YL8rCPgdiwiRXqYpSqk1sjdYizjOBJ-bGxMyYHvDu6cus3Z_dtQk5l50Q74dc4kY63EiPmx6EC8up36NkBRvRAUx2NbjIGhKJdAVb_idbW7Xhr5KhrJik8rjZ_QnBz5zKBj_34MPCss3wfOa24rhbHczlYqglxnvwZnEYY6R78aUKW879OchDgooX_77Ea9hAB5Ffh-PRS7jvhva6zS1Yr2dzuw33sst6Us1eNb5O4PttO8oNf2iYNQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=StRegA%3A+Unsupervised+anomaly+detection+in+brain+MRIs+using+a+compact+context-encoding+variational+autoencoder&rft.jtitle=Computers+in+biology+and+medicine&rft.au=Chatterjee%2C+Soumick&rft.au=Sciarra%2C+Alessandro&rft.au=D%C3%BCnnwald%2C+Max&rft.au=Tummala%2C+Pavan&rft.date=2022-10-01&rft.pub=Elsevier+Ltd&rft.issn=0010-4825&rft.eissn=1879-0534&rft.volume=149&rft_id=info:doi/10.1016%2Fj.compbiomed.2022.106093&rft.externalDocID=S0010482522008010 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F00104825%2Fcov200h.gif |