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dc.contributor.authorMahootiha, Maryamalsadat
dc.contributor.authorQadir, Hemin Ali
dc.contributor.authorBergsland, Jacob
dc.contributor.authorBalasingham, Ilangko Sellappah
dc.date.accessioned2024-04-05T09:04:00Z
dc.date.available2024-04-05T09:04:00Z
dc.date.created2024-01-23T10:37:47Z
dc.date.issued2023
dc.identifier.isbn9783031345852
dc.identifier.issn1867-8211
dc.identifier.urihttps://hdl.handle.net/11250/3125016
dc.description.abstractDeep learning (DL) has proven itself as a powerful tool to capture patterns that human eyes may not be able to perceive when looking at high-dimensional data such as radiological data (volumetric data). For example, the classification or grading of kidney tumors in computed tomography (CT) volumes based on distinguishable patterns is a challenging task. Kidney tumor classification or grading is clinically useful information for patient management and better informing treatment decisions. In this paper, we propose a novel DL-based framework to automate the classification of kidney tumors based on the International Society of Urological Pathology (ISUP) renal tumor grading system in CT volumes. The framework comprises several pre-processing techniques and a three-dimensional (3D) DL-based classifier model. The classifier model is forced to pay particular attention to the tumor regions in the CT volumes so that it can better interpret the surface patterns of the tumor regions to attain performance improvement. The proposed framework achieves the following results on a public dataset of CT volumes of kidney cancer: sensitivity 85%, precision 84%. Code used in this publication is freely available at: https://github.com/Balasingham-AI-Group/Classification-Kidney-Tumor-ISUP-Grade.en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofPervasive Computing Technologies for Healthcare 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings
dc.titleClassification of Kidney Tumor Grading on Preoperative Computed Tomography Scansen_US
dc.title.alternativeClassification of Kidney Tumor Grading on Preoperative Computed Tomography Scansen_US
dc.typeChapteren_US
dc.description.versionpublishedVersionen_US
dc.rights.holderThis version of the article is not available due to the publisher copyright restrictions.en_US
dc.source.pagenumber75-89en_US
dc.identifier.doi10.1007/978-3-031-34586-9_6
dc.identifier.cristin2232745
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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