Cancers, Free Full-Text
Por um escritor misterioso
Last updated 10 julho 2024
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Urinary cytology is a useful, essential diagnostic method in routine urological clinical practice. Liquid-based cytology (LBC) for urothelial carcinoma screening is commonly used in the routine clinical cytodiagnosis because of its high cellular yields. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to integrate new deep learning methods that can automatically and rapidly diagnose a large amount of specimens without delay. The goal of this study was to investigate the use of deep learning models for the classification of urine LBC whole-slide images (WSIs) into neoplastic and non-neoplastic (negative). We trained deep learning models using 786 WSIs by transfer learning, fully supervised, and weakly supervised learning approaches. We evaluated the trained models on two test sets, one of which was representative of the clinical distribution of neoplastic cases, with a combined total of 750 WSIs, achieving an area under the curve for diagnosis in the range of 0.984–0.990 by the best model, demonstrating the promising potential use of our model for aiding urine cytodiagnostic processes.
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Cancer Free Posters for Sale
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Destination Healthy Skin Toolkit - The Skin Cancer Foundation
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Cancers, Free Full-Text
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Weekly Paclitaxel in the Adjuvant Treatment of Breast Cancer
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Cancers, Free Full-Text
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Cancers, Free Full-Text
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The association between telomere length and cancer risk in population studies
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Cancers, Free Full-Text
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Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study - The Lancet Digital Health
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