2021
DOI: 10.3389/fonc.2021.746750
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Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning

Abstract: ObjectivesThis study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.MethodsTraining and validation datasets were established based on radiological, clinical, and pathological data exported from the radiology, urolog… Show more

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Cited by 9 publications
(1 citation statement)
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“…Teng et al. ( 32 ) used a total of six deep learning models to identify pRCC and chRCC. They extracted four case samples from The Cancer Imaging Archive (TCIA), a public database of cancer images, to participate in forming an external test set, and the best model (MobileNetV2) achieved 96.9% accuracy in the validation set (99.4% of sensitivity and 94.1% of specificity) and 100% (case accuracy)/93.3% (image accuracy) in the test set.…”
Section: Deep Learning To Identify Rcc Pathological Subtypesmentioning
confidence: 99%
“…Teng et al. ( 32 ) used a total of six deep learning models to identify pRCC and chRCC. They extracted four case samples from The Cancer Imaging Archive (TCIA), a public database of cancer images, to participate in forming an external test set, and the best model (MobileNetV2) achieved 96.9% accuracy in the validation set (99.4% of sensitivity and 94.1% of specificity) and 100% (case accuracy)/93.3% (image accuracy) in the test set.…”
Section: Deep Learning To Identify Rcc Pathological Subtypesmentioning
confidence: 99%