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dc.contributor.authorWetteland, Rune
dc.contributor.authorEngan, Kjersti
dc.contributor.authorEftestøl, Trygve Christian
dc.contributor.authorKvikstad, Vebjørn
dc.contributor.authorJanssen, Emiel
dc.contributor.authorTøssebro, Erlend
dc.contributor.authorLillesand, Melinda
dc.identifier.citationWetteland, R., Kvikstad, V., Eftestøl, T., Tøssebro, E., Lillesand, M., Janssen, E.A.M., Engan, K. (2021) Automatic diagnostic tool for predicting cancer grade in bladder cancer patients using deep learning. IEEE Access, 9, pp. 115813 - 115825en_US
dc.description.abstractThe most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low- and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAutomatic diagnostic tool for predicting cancer grade in bladder cancer patients using deep learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.subject.nsiVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.subject.nsiVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Onkologi: 762en_US
dc.source.pagenumber115813 - 115825en_US
dc.source.journalIEEE Accessen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal