Firstly, we introduce the proposed hybrid CNN architecture and local/global branches. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images … Mach Learn. 2016; 35(11):2369–80. Unlike the augmentation methods (rotation with fixed angles) in [12], we rotate the images randomly. Based on PS, the global patient recognition rate is defined as. Bayramoglu N, Kannala J, Heikkilä J. Breast Cancer Histopathological Image Classification: A Deep Learning Approach. Then, we could find out the differences of supporting areas when making decision between pathologists and algorithms. YW collected the data and conduct image preprocessing and augmentation it. The initial starting learning rate is 0.0004 and then it decreases exponentially every 10000 iterations. Second, by embedding the proposed Squeeze-Excitation-Pruning (SEP) block into our hybrid model, the channel importance can be learned and the redundant channels are thus removed. By local voting and two-branch information merging, our hybrid model obtains stronger representation ability. Breiman L. Bagging predictors. A dynamic and more efficient method is proposed to prune neural network weights in [25]. Most of the work has been conducted on well-known datasets like MIAS and DDSM along with some histopathological images. This work is supported in part by the Beijing Natural Science Foundation (4182044) and basic scientific research project of Beijing University of Posts and Telecommunications (2018RC11). Finally, the pathologists finish diagnosis through visual inspection of histological slides under the microscope. Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polónia A, Campilho A. In: Neural Networks (IJCNN), 2016 International Joint Conference On. 3(b). © 2021 BioMed Central Ltd unless otherwise stated. Article  The design of this study is based on public datasets, and all these datasets are allowed for academic use. The experimental results are compared against the existing machine learning and deep learning-based approaches with respect to image-based, patch-based, image-level, and patient-level classification. Article  In: 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). 1. where Nall is the number of cancer images of the test set and Nrec is the correctly classified cancer images. International Joint Conference on Neural Networks (IJCNN) 2016; 2560-2567. NIH In our experiment, BACH WSI dataset is selected to test the algorithm. The detailed channel pruning process will be discussed in compact model design part. the Inception module), they are first passed through a squeezing operation, which aggregates the feature maps across spatial dimensions W×H to produce a 1×1×C channel descriptor. Guo Y, Yao A, Chen Y. In this work, we propose an algorithm for training deep neural networks for classification of breast cancer in histopathological images affected by data unbalance with support of active learning. BME 2018. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. features extraction from breast cancer images. 2002; 52(1):8–22. The inter- and intraobserver reproducibilities of the histopathological systems of breast cancer classification suggested by the World Health Organisation (WHO), the Armed Forces Institute of … On the other hand, the downsampled input image as a whole is put into the global model branch and the prediction PG is obtained. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Automated classification of cancers using histopathological images … Overall, 200 × magnification factor shows a higher potential than the other magnification factors. 7. The strategies we used include random rotation, flipping transformation and shearing transformation. In: Pattern Recognition (ICPR), 2016 23rd International Conference On. The designed CNN architecture. After that, the tissue is cut by a high precision instrument and mounted on glass slides. According to the figure, we can see that there are many channels with low importance, which means these channels are redundant and thus can be pruned. Texture CNN for Histopathological Image Classification. The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients …

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