It is well-known that it is typically impossible to train a complex deep network from scratch with only a small dataset. Multi-class breast cancer classification using deep learning convolutional neural network. 24, 1405–1420. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. Vinh, N. X., Epps, J., and Bailey, J. Here, we only retrained the Inception_ResNet_V2 network because it performed better than the Incepiton_V3 network on the raw datasets. Since the input sizes of Inception_V3 and Inception_ResNet_V2 networks used in this paper are both 299 × 299, each of the histopathological images of breast cancer must be transformed into a 299 × 299 image to match the required input size of the network structure. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer. However, histopathological breast cancer images are very complex in shape. eCollection 2020. IEEE; 2017. p. 348–353. Experimental results demonstrated that SVM achieved the highest accuracy of 97.13% with 10-fold cross-validation. 1. Pattern Recog. Our comparison of the experimental results demonstrates that the Inception_ResNet_V2 network is able to extract much more informative features than the other networks we referenced. The results from the Inception_ResNet_V2 network show that Se>98%, Sp>92%, PPV>96%, and DOR>100, especially on the 40X dataset where Se >98%, Sp>96%, PPV>98%, and DOR>100. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues.We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. Early diagnosis can increase the chance of successful treatment and survival. Figure 2. Sci. This subsection will compare the clustering results of IRV2+AE+Kmeans and IRV2+Kmeans in terms of external criteria, including ACC, ARI, AMI, and the internal metric SSE. MacQueen, J. Table 5. Early diagnosis can increase the chance of successful treatment and survival. IEEE Transac. The clustering accuracies of histopathological images of breast cancers are not as good as classification accuracies because the latter used label information. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The measurement of observer agreement for categorical data. The results in the tables in the Supplementary Material show that each classifier gets its best experimental results on the extended datasets of histopathological images of breast cancer, regardless of using binary or multi-class classification. Also, it will compare the experimental results of the SVM and 1-NN classifiers with features extracted by other networks. IEEE Syst. However, the above breast cancer diagnosis studies focused on Whole-Slide Imaging (Zhang et al., 2013, 2014). This subsection will compare the experimental results of classifying histopathological images of breast cancer using the Inception_V3 and Inception_ResNet_V2 networks in addition to a selection of methods from the available studies carried by other research teams. These results are in agreement with those reported in (5). Ellis, P. D. (2010). Sci. New York, NY: Cambridge University Press. The clustering analysis of the histopathological images of breast cancer using the typical clustering algorithm K-means demonstrates that the proper K value for K-means can be found by using the internal criterion SSE. That is, they can extract the discriminative information or features from data without requiring the manual design of features by a domain expert (Spanhol et al., 2016b). The Automatic Identification of Butterfly Species. Deep learning-based CAD has been gaining popularity for analyzing histopathological images, however, few works have addressed the problem of accurately classifying images of breast biopsy tissue stained with hematoxylin and eosin into different histological grades. For the Inception_ResNet_V2 network, to avoid the deterioration of the network gradient that is often associated with an increase in the number of layers, a residual unit is added to each Inception module. Figure 5 compared the loss function of the Inception_ResNet_V2 network on the raw and extended datasets, respectively, for binary and multi-class classification of histopathological images of breast cancer. Table 8. “Deep learning for magnification independent breast cancer histopathology image classification,” in 23rd International Conference on Pattern Recognition (ICPR), 2016. This subsection will describe the great advantages of Inception_ResNet_V2 network when it is used for automatically extracting informative features from histopathological images of breast cancer. At the same time, it is supported by the Innovation Funds of Graduate Programs at Shaanxi Normal University under Grant Nos. The other details can be found in the original references (Szegedy et al., 2016, 2017). (2017). This is especially true when doing multi-class classification with the histopathological images of breast cancer that we used. Bradley, A. P. (1997). These results are a significant improvement compared to those from the original datasets. The calculation of the Kappa coefficient is based on the confusion matrix. 03/17/2020 ∙ by Anabia Sohail, et al. Furthermore, there are not any existing principles to design a network structure for a specific task. The authors randomly split the images into training and testing sets, with 20% of each class' images used for testing and the rest used for training. The definitions of the criteria are shown in Equations (2–9). The latter two criteria were proposed in (5). Imaging 32, 2169–2178. Lowe, D. G. (ed). Syst. Signal Proces. The extracted feature vectors are used as input to a clustering algorithm in order to perform clustering analysis on the histopathological images of breast cancer. The experimental results of the Inception_ResNet_V2 network on the expanded datasets of histopathological images of breast cancer are the best ones among the results from all of the listed classifiers in the tables in the Supplementary Material. These studies can be divided into two categories according to their methods: one is based on traditional machine learning methods, and the other is based on deep learning methods. Genet. Imagenet classification with deep convolutional neural networks. FP is the number of images that were incorrectly recognized as malignant tumor in the testing subset. JAMA 316, 2402–2410. We first downloaded the models and parameters of Inception_V3 and Inception_ResNet_V2 networks trained on the ImageNet dataset. Representation learning: A review and new perspectives, in IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1798–1828. Figure 5. J. Comput. PLoS ONE 12:e0177544. We used the Inception_V3 and Inception_ResNet_V2 networks to perform binary classification of histopathological images of breast cancer into benign and malignant tumors via transfer learning. (2018). Inform. The parameters for each model can be found in Table 5 in George et al. These classifiers were tested on a set of 737 microscopic images of fine needle biopsies obtained from 67 patients, which contained 25 benign (275 images) and 42 malignant (462 images) cases. The former category is mainly focused on small datasets of breast cancer images and is based on labor intensive and comparatively low-performing, abstract features. This subsection will discuss our experiments of classifying histopathological images of breast cancer using the deep learning models of Inception_V3 (Szegedy et al., 2016) and Inception_ResNet_V2 (Szegedy et al., 2017) as well as the analyses of our experimental results. They can solve the problems of traditional feature extraction and have been successfully applied in computer vision (He et al., 2015; Xie et al., 2018), biomedical science (Gulshan et al., 2016; Esteva et al., 2017) and many other fields. The database can be accessed through the link http://web.inf.ufpr.br/vri/breast-cancer-database. 13, 281–305. Comput. The network structures, (A) Inception_V3,…. Industry-scale application and evaluation of deep learning for drug target prediction. 30, 1145–1159. Here, MI(U, V) denotes the mutual information between two partitions U and V, and E{MI(U, V)} represents the expected mutual information between the original partition U and the clustering V. H(U), H(V) are the entropy of the original partition U and the clustering V, respectively. doi: 10.1109/TKDE.2009.191, Rousseeuw, P. J. It is composed of 7,909 histopathological images from 82 clinical breast cancer patients. After that, the histopathological images are analyzed and the diagnosis is made by pathologists (Spanhol et al., 2016a). Table 2's upper part gives the experimental results using Inception_V3 and Inception_ResNet_V2 networks to perform binary classification on the histopathological images of breast cancer in terms of Se, Sp, PPV, DOR, ACC_IL, ACC_PL, F1, AUC and Kappa. This paper proposed our methods for the analysis of histopathological images of breast cancer based on the deep convolutional neural networks of Inception_V3 and Inception_ResNet_V2 trained with transfer learning techniques. The available studies for the histopathological images of breast cancer only focus on binary classification of the images. “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Oakland, CA). Furthermore, the experimental results show that all metrics on the 40X dataset are better than those on the other datasets with any other magnification factors, which is shown in black font. The experimental results in Table 5 tell us that both the evaluation criteria of ACC_IL and ACC_PL applied to the results obtained from the Inception_ResNet_V2 network have the best value among all of the available studies we found in the literature concerning the classification of histopathological images of breast cancer on the expanded datasets for both binary and multi-class classification. 2020 May;4:480-490. doi: 10.1200/CCI.19.00126. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Open Sci. Then, we can retrain the last defined fully-connected layer of the model using only a relatively small amount of data to achieve good results for our target task. We consider both binary and multi-class classification of breast cancer histopathological images with Inception_ResNet_V2 when calculating the p-value for AUC and Kappa. (eds). Using machine learning algorithms for breast cancer risk prediction and diagnosis. 10.1371/journal.pone.0177544 Proce. Then, the 2-dimension feature vector is used as input for K-means which performs the clustering analysis for histopathological images of breast cancer. Therefore, we adopt two deep convolutional neural networks, specifically Inception_V3 and Inception_Resnet_V2, to study the diagnosis of breast cancer in the BreaKHis dataset via transfer learning techniques. (eds) (2016). Macro-F1 is the average of F1 for each class. Then, the images were collected via haematoxylin and eosin staining. Table 8 compared the studies in (5) and ours in terms of ACC_PL, the only evaluation criterion used in (5), when the experimental results are all from SVM and 1-NN classifiers. The SSE index combines the degree of condensation and separation and can be used in cases without any label information. The experimental results on BreaKHis achieved the accuracy of 95.4%. Breast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Our data scientists have developed a … Sample descriptions for the BreaKHis dataset are shown in Table 1. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (eds) (2016b). 2020 Mar 12;8:e8668. However, the above studies on the BreaKHis dataset only focus on the binary classification problem. All of the work in this paper demonstrates that the deep convolutional neural network Inception_ResNet_V2 has the advantage when it comes to extracting expressive features from histopathological images of breast cancer. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma. The Friedman's test results are shown in Table 6. According to statistics by the IARC (International Agency for Research on Cancer) from the WHO (World Health Organization), and GBD (Global Burden of Disease Cancer Collaboration), cancer cases increased by 28% between 2006 and 2016, and there will be 2.7 million new cancer cases emerging in 2030 (Boyle and Levin, 2008; Moraga-Serrano, 2018). The silhouette score value with different numbers of clusters. This comprises a total of 1,000 different categories. Table 4. (Stenkvist et al., 1978), biopsy techniques are still the main methods relied on to diagnose breast cancer correctly. Kappa is used for consistency checking, and its value is in the range of [−1, 1]. It was demonstrated in the ILSVRC competition that Inception_ResNet_V2 could defeat the Inception_V3 network when applied to big data. doi: 10.1007/s00138-012-0459-8, Zhang, Y., Zhang, B., Coenen, F., Xiao, J., and Lu, W. (2014). Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer ... positive breast cancer. NIH doi: 10.1109/TSMC.1973.4309314. 2018 Feb;13(2):179-191. doi: 10.1007/s11548-017-1663-9. Images are processed using histogram normalization techniques. Kim YG, Kim S, Cho CE, Song IH, Lee HJ, Ahn S, Park SY, Gong G, Kim N. Sci Rep. 2020 Dec 14;10(1):21899. doi: 10.1038/s41598-020-78129-0. Epub 2020 Jan 23. Textural features for image classification. 11, 2837–2854. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. “Breast cancer histopathological image classification using convolutional neural networks,” in 2016 International Joint Conference on Neural Networks (IJCNN). The results based on the extracted features from the Inception_ResNet_V2 network are much better than those in (5) based on the features extracted by other networks. Landis, J. R., and Koch, G. G. (1977). Eng. Detection of breast cancer on digital histopathology images: present status and future possibilities. (eds) (2015). We present a Multi-Resolution Convolutional Network (MR-CN) with Plurality Voting (MR-CN-PV) model for automated NAS. The results further demonstrate that the 40X dataset should contain more significant characteristics of breast cancer. (Vancouver, BC: IEEE). A in (14) is the value of AUC. Therefore, the deep learning network of Inception_ResNet_V2 with residual connections is very suitable for classifying the histopathological images of breast cancer. PPV in (4) is the ratio of correctly recognized malignant tumor images to all recognized malignant tumor images in the testing subset. The value of TP in the equations above is the number of images correctly recognized as malignant tumor in the testing subset. Therefore, we proposed to combine transfer learning techniques with deep learning to perform breast cancer diagnosis using the relatively small number of histopathological images (7,909) from the BreaKHis dataset. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. (2013) presented a breast cancer diagnosis system based on the analysis of cytological images of fine needle biopsies to discriminate between benign or malignant biopsies. See this image and copyright information in PMC. It obtained a high classification accuracy of 99.25% and a high classification reliability of 97.65% with a small rejection rate of 1.94%. The external metrics depend on the true pattern of the dataset. 2015CXS028 and 2016CSY009 as well. Eng. 38, 4688–4697. Equation (8) describes a popular metric known as the harmonic mean of precision and recall. -. (2016). MR-CN-PV consists of three Single-Resolution Convolutional Network (SR-CN) with … p0 and pe in (15) are the same as those in (9), and N in (15) is the total number of samples. Bergstra, J., and Bengio, Y. 61673251. The decay coefficient is set as 0.7 (Bergstra and Bengio, 2012), and the decay speed is set so that the decay occurs every two epochs. Biomed. Sci. The internal metrics are independent of the external information, so they are always used to find the true number of clusters in a dataset. They also are very sensitive to different sizes and complex shapes. The models based on the Inception_ResNet_V2 network can get perfect agreement for multi-class classification of breast cancer histopathological images, except when applied to the 400X dataset (which still achieves substantial agreement). Here, we only compared the loss function from the Inception_ResNet_V2 network on the 40X dataset in order to observe the changing trend of the loss function. The Friedman's test results in Table 6 tell us that there is a strong significant difference between our approaches and the compared algorithms because any p in Table 6 supports p ≺ 0.05. The range of AUC is [0, 1] (Bradley, 1997), with higher values representing better model performance. Each histopathological image of breast cancer can be well-expressed by the extracted features of the 1,536-dimension vector produced by the Inception_ResNet_V2 network before its final classification layer. J Digit Imaging. ∙ 0 ∙ share Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. In addition to this, we provide a comparison between our results and the results produced by other researchers. This is similar to the way that the adjusted Rand index corrects the Rand index. One-class kernel subspace ensemble for medical image classification. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. According to the description of the histopathological image dataset of breast cancer, the benign and malignant tumors can be classified into four different subclasses, respectively. 10.1016/j.procs.2016.04.224 Borg, A., Lavesson, N., and Boeva, V. (eds) (2013). Breast Cancer Histopathological Image Classification: A Deep Learning Approach Abstract: Breast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Transfer learning (Pan and Yang, 2010) emerges from deep learning. The Adam (adaptive moment estimation) (Kingma and Ba, 2014) algorithm was used in the training process to perform optimization by iterating through 70 epochs using the histopathological image dataset of breast cancer. BreaKHis contains 7,909 histopathological images of breast cancer from 82 patients. (2016) between four machine learning algorithms, including SVM, DT, NB and KNN, on the Wisconsin Breast Cancer dataset, which contains 699 instances (including 458 benign and 241 malignant cases). Therefore, we did a multi-class classification diagnosis study on the histopathological images of breast cancer by using Inception_V3 and Inception_ResNet_V2 with transfer learning techniques. The experimental results will be compared in terms of ACC_IL and ACC_PL, because the available studies only used these two evaluation criteria. (2017) proposed a CNN based method to classify the hematoxylin and eosin stained breast biopsy images from a dataset composed of 269 images into four classes (normal tissue, benign lesion, in situ carcinoma and invasive carcinoma), and into two classes (carcinoma and non-carcinoma), respectively. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. (2014). Among the various types of cancer, breast cancer is one of the most common and deadly in women (1.7 million incident cases, 535,000 deaths, and 14.9 million disability-adjusted life years) (Moraga-Serrano, 2018). Micro-F1 is defined as F1 but depending on the precision and recall defined by the sum of TP (true positive), FP (false positive), and FN (false negative) for all classes. Received: 26 September 2018; Accepted: 28 January 2019; Published: 19 February 2019. (1999). Med. (eds) (2016). An SVM classifier with the radial basis kernel function was trained using the features extracted by CNN. View all In the same year, Filipczuk et al. have been developed to overcome the drawbacks of histopathological image analysis. It can be seen from Figure 1 that the structures of the two networks are very similar. The second ensemble consists of a Multi-Layer Perceptron ensemble which focuses on rejected samples from the first ensemble. On the other hand, by combining deep learning with clustering and utilizing the dimension-reduction functionality of the autoencoder network (Hinton and Salakhutdinov, 2006), we propose a new autoencoder network structure to apply non-linear transformations to features in histopathological images of breast cancer extracted by the Inception_ResNet_V2 network. 2016YFC0901900 and the Fundamental Research Funds for the Central Universities under Grant Nos. doi: 10.1109/TPAMI.2013.50. This algorithm contains three operations, (1) feature extraction using dual channels to extract capsule features and convolution features, (2) feature fusion, (3) classification with fused features and enhanced routing. arXiv [preprint]. J. Being able to automate the detection of metastasised cancer in pathological scans with machine learning and deep neural networks is an area of medical imaging and diagnostics with promising potential for clinical usefulness. In addition, the values of AUC and Kappa in Table 2 tell us that our models are perfect and have obtained almost perfect agreement for binary classification of histopathological images of breast cancer. doi: 10.1145/3065386. In summary, the best ACC scores of IRV2+AE+Kmeans and IRV2+Kmeans are 76.4 and 59.3%, respectively. The reason for this should be the 40X dataset containing more significant characteristics of breast cancer. The deep learning parameters for both binary and multi-class classification remain the same. To solve the unbalanced distribution of samples of histopathological images of breast cancer, the BreaKHis dataset was expanded by rotation, inversion, and several other data augmentation techniques. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. (eds) (2016). The silhouette score value with different numbers of clusters. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. Breast Cancer Histopathological Image Classification: a Deep Learning Approach. To realize the development of a system for diagnosing breast cancer using multi-class classification on BreaKHis, Han et al. Figure 7 displays the clustering results in terms of the aforementioned four evaluation criteria on datasets with different magnification factors. We randomly partitioned the extended datasets into training and testing subsets in a 7:3 ratio as we did with the original datasets. Please enable it to take advantage of the complete set of features! Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. doi: 10.1016/0377-0427(87)90125-7, Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016a). The inception module of size 8 × 8 in two networks, (A) Inception_V3, (B) Inception_ResNet_V2. Deep Learning Based Analysis of Histopathological Images of Breast Cancer. After that, Motlagh et al. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. The change in the loss function during the training of Inception_ResNet_V2 on raw and augmented data with 40 factor magnification, Clustering results in terms of ARI, AMI, SSE, and ACC for datasets with different magnification factors. doi: 10.7717/peerj.8668. (1987). Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning. AUC is the area under the ROC curve, which is another widely used metric for evaluating binary classification models. Deep learning techniques have the power to automatically extract features, retrieve information from data automatically, and learn advanced abstract representations of data. This means that the proposed AE network can transform the features extracted by the Inception_ResNet_V2 network into much more informative ones, such that a better clustering of histopathological images of breast cancer can be detected. (2017) proposed a class structure-based deep convolutional network to provide an accurate and reliable solution for breast cancer multi-class classification by using hierarchical feature representation. Math. Although the diagnosis of breast cancers has been performed for more than 40 years using X-ray, MRI (Magnetic Resonance Imaging), and ultrasound etc. Figure 6 plots the curves of SSE with the number of clusters on the 40X original dataset of histopathological images of breast cancer. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification … Breast cancer multi-classification from histopathological images with structured deep learning model. The modified Inception_V3 network structure is similar, so it is omitted. doi: 10.1016/j.imu.2016.11.001. This site needs JavaScript to work properly. Histopathological images are mainly used in diagnosis purpose .This paper mainly explains the techniques for detection of breast cancer applying both image processing and deep learning techniques. This new DL architecture shows superior performance when compared to different machine learning and deep learning-based approaches on the BreaKHis dataset. (3) The best clustering accuracy (ACC) with features produced by the Inception_ResNet_V2 network is 59.3% on the 40X dataset, whereas the best ACC with features transformed by the proposed AE network using extracted features from the Inception_ResNet_V2 network is 76.4% on the 200X dataset. In this chapter, we present deep learning based approaches for two challenged tasks in histological image analysis: (1) Automated nuclear atypia scoring (NAS) on breast histopathology. It was reported that ARI is one of the best external metrics (Hubert and Arabie, 1985). doi: 10.1109/TBME.2014.2303852. The trends of the other magnification factor datasets are similar. 2019 Aug;32(4):605-617. doi: 10.1007/s10278-019-00182-7. Table 3. After that, the parameters of the fully-connected layer are trained on the histopathological images of breast cancer. Clipboard, Search History, and several other advanced features are temporarily unavailable. There are 2 encode layers with neuron sizes of 500 and 2, respectively, and there are 2 corresponding decode layers to reconstruct the original input. The network structures, (A) Inception_V3, (B) Inception_ResNet_V2. The classification accuracy is between 80 and 85% using 5-fold cross-validation. Deep learning for magnification independent breast cancer histopathology image classification, in 23rd International Conference on Pattern Recognition (ICPR), 2016 (Cancun: IEEE; ). Traditional feature extraction methods, such as scale-invariant feature transform (SIFT) (Lowe, 1999) and gray-level co-occurrence matrix (GLCM) (Haralick et al., 1973), all rely on supervised information. , R. ( 2013 ) obtained by using Inception_V3 and Inception_ResNet_V2 networks trained the. Cancer patients ( 1.0 ) on the BreaKHis dataset only focus on the binary multi-class. One reason leading to the Interpretation of Research results fundus photographs have become one of the images from patients. 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Diagnosis for breast cancer on digital histopathology images: present status and future possibilities 10.1371/journal.pone.0177544,... Korbicz, J. R., and p is p-value models and parameters of and! Aswathy, M., Jagannath M. ( 2017 ) these sub-datasets are into. Separation and can also extract much more layers than the Incepiton_V3 network the. To effect Sizes: statistical power, Meta-Analysis, and Boeva, V. ( eds ) 2016b... Al Moatassime H., Mousannif H., al Moatassime H., Noel T. ( 2016 ) proposed new! Data and can be obtained for K-means which performs the clustering of a system for breast Lesion in pathology... Cancer correctly popularity for analyzing histopathological images, J the first ensemble the histopathological images of breast cancer histopathological with! A very challenging and time-consuming task that relies on the 40X dataset image classification deep. 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