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dc.contributor.advisorEngan, Kjersti
dc.contributor.advisorEftestøl, Trygve Christian
dc.contributor.authorFuster Navarro, Saul
dc.date.accessioned2024-05-13T09:48:30Z
dc.date.available2024-05-13T09:48:30Z
dc.date.issued2024
dc.identifier.citationDeep Learning-Driven Diagnostic and Prognostic Solutions for Histopathological Images of Bladder Cancer by Saul Fuster Navarro, Stavanger : University of Stavanger, 2024 (PhD thesis UiS, no. 763)en_US
dc.identifier.isbn978-82-8439-241-7
dc.identifier.issn1890-1387
dc.identifier.urihttps://hdl.handle.net/11250/3130049
dc.descriptionPhD thesis in Information technologyen_US
dc.description.abstractThis thesis presents a comprehensive investigation into the development and application of advanced computational techniques for the extraction of crucial diagnostic and prognostic information from histological images of non-muscle invasive bladder cancer (NMIBC). Computational pathology (CPATH) relies on digitized high-resolution tissue samples, referred to as whole slide images (WSIs). Histological examination of WSIs plays a pivotal role in the diagnosis and prognosis of NMIBC. The primary focus of this research is the utilization of deep learning algorithms to automatically analyze histological images and extract visual cues with diagnostic and prognostic significance. With respect to diagnostics, several convolutional neural network architectures are designed and trained on diverse datasets of NMIBC tissue specimens to identify and classify key histological features, including tumor grading and staging. Moreover, the variability of histological visual features between pathology laboratories during the training of convolutional neural network (CNN) models is questioned. Emphasis is placed on the development of label-efficient guidelines for domain-adapting deep learning models. In addition, an architecture for machine learning is introduced to stratify regions of interest (ROIs) in weakly supervised learning. This additional data stratification aids in localizing ROIs and mitigating cross-noise variability among them. Furthermore, this thesis explores the integration of deep learning techniques for prognostic assessment. Through an analysis of the relative spatial distribution among urothelium and contingent stromal immune cells, our model predicts patient treatment outcomes and the likelihood of recurrence with a high degree of precision. These prognostic models provide invaluable support to clinicians in customizing personalized treatment strategies and offering patient counseling. The work presented in this thesis represents a substantial advancement toward improving the diagnostic and prognostic capabilities in the management of NMIBC. Leveraging the potential of computational analysis, we offer pathologists state-of-the-art tools to augment diagnostic precision and optimize patient care, ultimately contributing to better outcomes and quality of life for individuals affected by this prevalent form of bladder cancer.en_US
dc.language.isoengen_US
dc.publisherUniversity of Stavanger, Norwayen_US
dc.relation.ispartofseriesPhD thesis UiS;
dc.relation.ispartofseries;763
dc.relation.haspartPaper 1: Fuster, S., Eftestøl, T., & Engan, K. (2022, December). Nested multiple instance learning with attention mechanisms. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 220-225).en_US
dc.relation.haspartPaper 2: Fuster, S., Khoraminia, F., Eftestøl, T., Zuiverloon, T. C., & Engan, K. (2023, September). Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images. In 2023 31st European Signal Processing Conference (EUSIPCO) (pp. 1045-1049).en_US
dc.relation.haspartPaper 3: Fuster, S., Khoraminia, F., Kiraz, U., Kanwal, N., Kvikstad, V., Eftestøl, T., ... & Engan, K. (2022, June). Invasive cancerous area detection in Non-Muscle invasive bladder cancer whole slide images. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) (pp. 1-5).en_US
dc.relation.haspartPaper 4: Fuster, S., Kiraz, U., Eftestøl, T., Janssen, E. A. M., Engan, K. NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning. Under review.en_US
dc.relation.haspartPaper 5: Fuster, S., Khoraminia, F., Silva-Rodríguez, J., Kiraz, U., van Leenders, G. J. L. H., Eftestøl, T., Naranjo, V., Janssen, E. A. M., Zuiverloon, T.C.M., Engan, K. Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images Under review.en_US
dc.rightsCopyright the author
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectinformasjonsteknologien_US
dc.subjectblærekreften_US
dc.subjectpatologien_US
dc.subjectpathologyen_US
dc.subjectbladder canceren_US
dc.titleDeep Learning-Driven Diagnostic and Prognostic Solutions for Histopathological Images of Bladder Canceren_US
dc.typeDoctoral thesisen_US
dc.rights.holder© 2024 Saul Fuster Navarroen_US
dc.subject.nsiVDP::Mathematics and natural science: 400::Information and communication science: 420en_US
dc.subject.nsiVDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Generell patologi, patologisk anatomi: 719en_US


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