Deep Learning in Liver Biopsies using Convolutional Neural Networks

Arjmand, Alexandros; Angelis, Constantinos T.; Tzallas, Alexandros T.; Tsipouras, Markos G.; Glavas, Euripidis; Forlano, Roberta; Manousou, Pinelopi; Γιαννακέας, Νικόλαος

doi:10.1109/tsp.2019.8768837

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…The current study is an extension of an earlier project [25], the results of which were presented at 42nd International Conference on Telecommunications and Signal Processing (TSP) held in Budapest in July 2019. It focuses on resolving the aforementioned diagnostic barrier by fully automating the supervised classification process using deep learning systems.…”

Section: Discussionmentioning

confidence: 99%

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

et al. 2019

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Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

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Section: Discussionmentioning

confidence: 99%

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

et al. 2019

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“…The authors placed the normalization layer before the activation layer and the dropout layer in the middle of the convolution blocks. The main difference between our proposed architecture and the architecture proposed by Arjmand et al [22] relies on the number of convolution layers used. From the results obtained, it seems that the choice of the number of convolution layers plays a fundamental role in the performance of the model.…”

Section: Comparison Between Different Benchmark Cnnmentioning

confidence: 99%

“…The main contributions of this paper are as follows:5.6. Comparison between Different State-of-the-Art CNN Arjmand et al [22] presented a network with three convolution layers, where the number of kernels is 64, 32, and 16, respectively. The kernels size used was 5 × 5, 3 × 3, and 3 × 3, respectively.…”

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“…In particular, a network with just three convolution layers seems to be not sufficient for dealing with the complexity of the application at hand. The architecture of Arjmand et al [22] is shown in Figure 7.Lai et al [24] introduced a network with six convolution layers and no fully connected layers. This network achieved the best results, among the competitors of our proposed architecture, for both the optimizers.…”

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A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

Kandel

Castelli

2020

Applied Sciences

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Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.

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Grading of steatosis, fibrosis, lobular inflammation, and ballooning from liver pathology images using pre‐trained convolutional neural networks

Zamanian

Shalbaf

2023

Int J Imaging Syst Tech

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This study aims to automatically detect the degree of pathological indices as a reference method for detecting the severity and extent of various liver diseases from pathological images of liver tissue with the help of deep learning algorithms. Grading is done using a collection of pre‐trained convolutional neural networks, including DenseNet121, ResNet50, inceptionv3, MobileNet, EfficientNet‐b1, EfficientNet‐b4, Xception, NASNetMobile, and Vgg16. These algorithms are performed by fine‐tuning the trainable layers of the networks. The results showed that compared to other methods, the EfficientNet‐b1 network provides a better response to grade the stage of liver disease among all indicators from pathological images, due to its structural features. This classification accuracy was 97.26% for fibrosis, 94.1% for steatosis, 90.2% for lobular inflammation, and 98.0% for ballooning. Consequently, this fully automated framework can be very useful in clinical methods and be considered as an assistant or an alternative to the diagnosis of experienced pathologists.

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Deep Learning in Liver Biopsies using Convolutional Neural Networks

Cited by 8 publications

References 7 publications

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

Grading of steatosis, fibrosis, lobular inflammation, and ballooning from liver pathology images using pre‐trained convolutional neural networks

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