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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23318
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dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorMontoya, Javier-
dc.contributor.authorGruss, Jonathan-
dc.contributor.authorStebler, Yves D.-
dc.contributor.authorBozkir, Ahmet Selman-
dc.contributor.authorCalandri, Marco-
dc.contributor.authorSchwenker, Friedhelm-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2021-10-29T08:13:45Z-
dc.date.available2021-10-29T08:13:45Z-
dc.date.issued2021-10-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23318-
dc.description.abstractWith the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. for image-based diagnosis. Although the literature has shown promising efforts in this direction, reported results do not consider the variability of CT scans acquired under varying circumstances, thus rendering resulting models unfit for use on data acquired using e.g. different scanner technologies. While COVID-19 diagnosis can now be done efficiently using PCR tests, this use case exemplifies the need for a methodology to overcome data variability issues in order to make medical image analysis models more widely applicable. In this paper, we explicitly address the variability issue using the example of COVID-19 diagnosis and propose a novel generative approach that aims at erasing the differences induced by e.g. the imaging technology while simultaneously introducing minimal changes to the CT scans through leveraging the idea of deep autoencoders. The proposed prepossessing architecture (PrepNet) (i) is jointly trained on multiple CT scan datasets and (ii) is capable of extracting improved discriminative features for improved diagnosis. Experimental results on three public datasets (SARS-COVID-2, UCSD COVID-CT, MosMed) show that our model improves cross-dataset generalization by up to 11:84 percentage points despite a minor drop in within dataset performance.de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectAdaptive preprocessingde_CH
dc.subjectDomain adaptationde_CH
dc.subjectAutoencoderde_CH
dc.subject.ddc610.28: Biomedizin, Biomedizinische Technikde_CH
dc.titlePrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysisde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-23318-
zhaw.conference.details14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 23-25 October 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of CISP-BMEI’21de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.funding.zhawSynthetic data generation of CoVID-19 CT/X-rays images for enabling fast triage of healthy vs. unhealthy patientsde_CH
zhaw.funding.zhawStandardized Data and Modeling for AI-based CoVID-19 Diagnosis Support on CT Scans (SDMCT)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Amirian, M., Montoya, J., Gruss, J., Stebler, Y. D., Bozkir, A. S., Calandri, M., Schwenker, F., & Stadelmann, T. (2021, October). PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis. Proceedings of CISP-BMEI’21. https://doi.org/10.21256/zhaw-23318
Amirian, M. et al. (2021) ‘PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis’, in Proceedings of CISP-BMEI’21. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-23318.
M. Amirian et al., “PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis,” in Proceedings of CISP-BMEI’21, Oct. 2021. doi: 10.21256/zhaw-23318.
AMIRIAN, Mohammadreza, Javier MONTOYA, Jonathan GRUSS, Yves D. STEBLER, Ahmet Selman BOZKIR, Marco CALANDRI, Friedhelm SCHWENKER und Thilo STADELMANN, 2021. PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis. In: Proceedings of CISP-BMEI’21. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Oktober 2021
Amirian, Mohammadreza, Javier Montoya, Jonathan Gruss, Yves D. Stebler, Ahmet Selman Bozkir, Marco Calandri, Friedhelm Schwenker, and Thilo Stadelmann. 2021. “PrepNet : A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis.” Conference paper. In Proceedings of CISP-BMEI’21. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-23318.
Amirian, Mohammadreza, et al. “PrepNet : A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis.” Proceedings of CISP-BMEI’21, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021, https://doi.org/10.21256/zhaw-23318.


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