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The CNN model enabled classification of liver masses into 5 categories. The median area under the receiver operating characteristic curve to differentiate between categories was 0.92. Deep learning with CNN enabled classification of liver masses and differentiate between 5 categories at dynamic CT with high accuracy.
The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy ...
In this work, state-of-the-art Convolutional Neural Network (CNN) techniques areused to classify materials and also compare the results obtained by them.The ...
The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver ...
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The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy ...
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To develop and validate a proof-of-concept convolutional neural network (CNN)–based deep learning system (DLS) that classifies common hepatic lesions on ...
Oct 23, 2017 · In this study, we aimed to investigate the diagnostic performance of deep learning with a CNN for the differentiation of liver masses on dynamic ...
Nov 10, 2022 · A whole slide scan of a liver biopsy is analyzed by four dedicated convolutional neural networks (CNNs), which classify the features of ...
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Aug 31, 2024 · ALNE is a transformer-based deep learning model that can accurately distinguish autoimmune hepatitis and primary biliary cholangitis based on digital slides ...