breast cancer histopathological image classification

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. The 200 × magnification factor shows the best results among performances obtained with different magnification levels under 0.4 False Positive Rate (FPR). 2020 Nov;4:1039-1050. doi: 10.1200/CCI.20.00110. Texture CNN for Histopathological Image Classification. 12(b). ∙ 0 ∙ share . In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. By choosing a model trained by 40 × dataset, the performance with different pruning ratios is depicted in Fig. The breast histology microscopy we used in our work is stained by HE, and this staining method can help medical workers better observe the internal morphology of the tissue cells. With the increase of pruning ratio, our model will have the smallest amount of weights. The evaluation on the BACH dataset shows that the proposed hybrid model with multi-model assembling scheme outperforms the state-of-the-art work [11] in both patient level and image level accuracy. Epub 2020 Nov 5. 2018 Feb;13(2):179-191. doi: 10.1007/s11548-017-1663-9. CA: A Cancer J Clin. This site needs JavaScript to work properly. To make the model more compact, the other traditional compression scheme Dynamic Network Surgery (DNS) [25] method, which can properly incorporate connection splicing into the training process to avoid incorrect pruning, is merged with our method. Generally, great efforts and effective expert domain knowledge are required to design appropriate features for this type of method. Cite this article. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. The proposed scheme achieves promising results for the breast cancer image classification task. 6,402 TMA histopathologi-cal images were applied across lung, breast, lymphoma, and bladder cancer tissues. We should notice that for the first pruning loop, the related weights are produced by the initially pre-trained network. The excitation operation can explicitly model interdependencies between channels. We propose a novel compact breast cancer histopathology image classification scheme by assembling multiple compact hybrid CNNs. arXiv preprint arXiv:1602.02830. In work [9], the authors introduce a large, publicly available and annotated dataset, which is composed of 7909 clinically representative, microscopic images of breast tumor tissue images collected from 82 patients. To conduct breast cancer diagnosis, the materials obtained in the operating room are first processed by formalin and then embedded in paraffin [5]. 2002; 24(7):971–87. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. 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. - "A Dataset for Breast Cancer Histopathological Image Classification" Spanhol FA, Oliveira LS, Petitjean C, Heutte L: Breast cancer histopathological image classification using convolutional neural networks. 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. In work [11], the authors provide two strategies to generate the training samples: sliding window allowing 50% of overlap between patches; random extraction strategy with a fixed arbitrary number of patches (such as 1000) from each input image. Two model branches are integrated together to extract more key information, and the channel pruning module is embedded to compact the network. PubMed  technology extracts nucleus information from breast cancer histopathological images. FZS, HHD, and YG were responsible for the implementation of our algorithm. IEEE Trans Med Imaging. In our work, we use the activation factors si (i=1,2,...,C) obtained by SE block as channel weights in assisting the model compression. How to design a compact yet accurate CNN to alleviate the problems is still challenging. In: Pattern Recognition (ICPR), 2016 23rd International Conference On. Helsinki: ACM: 2014. p. 675–8. In work [11], the reported results are obtained by combining four patch-level models trained with different patch generation strategies, which produces the state-of-the-art for patient level result. The first objective of this paper is still to ensure accuracy like the other works, and we propose hybrid architecture and model assembling to achieve this goal. The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different … Blur insensitive texture classification using local phase quantization. To further gain accuracy from considerably increased depth and to make our model easier to optimize, we adopt residual networks (Inception-4c to Inception-4e, Inception-4d to SEP-4e) in the model. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification. To ensure a fair comparison, the experimental protocol proposed in [9] is strictly followed. Figure 1. YG analyzed the experiment result, and revised the writing. ... Keywords: histopathological image analysis, intraductal breast lesions, computer-aided diagnosis, ... it was not directly applicable to the histopathological classification … Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography. Chuang Zhu and Ying Wang are equal contributors. In: Proceedings of the 22nd ACM International Conference on Multimedia. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. features extraction from breast cancer images. Two convolution layers (conv1 and conv2) are selected and the importance of channels in each layer is visualized as Fig. The patient score (PS) is defined as, where NP is the number of cancer images for patient P and Nrec is the number of images that are correctly classified. They directly use the specific parameter of BN layers as the channel scaling factor to identify and remove the unimportant channels during training. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. (b) The basic structure of the SE block. Most of the work has been conducted on well-known datasets like MIAS and DDSM along with some histopathological images. 2017; 12(6):0177544. Generally, the key channels to the final classification results are prone to have higher activation factors and vice verse. Besides, we also show the results of using majority voting (Max) scheme when merging patch predictions, denoted as “2(Max)" and “3(Max)" in the table. 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 … (eds) 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7). Breast cancer is one of the most common and dangerous cancers impacting women worldwide. First, a hybrid CNN architecture is designed, which contains a global model branch and a local model branch. The early stage diagnosis and treatment can significantly reduce the mortality rate [3]. Deniz [9, 10,11,12,16]. HHD contributed to the building of the two-brunch model. By adopting the multi-model assembling scheme, our method outperforms the state-of-the-art in both patient level and image level accuracy for BACH dataset. In these studies, magnification factor based performances are given. Venice: IEEE: 2017. p. 2755–63. The SE block can adaptively recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels. Breast cancer has high morbidity and mortality among women according to the World Cancer Report [], and this type of cancer causes hundreds of thousands of deaths each year worldwide [].The early stage diagnosis and treatment can significantly reduce the mortality rate [].The histopathological diagnosis based on light microscopy is a gold standard for identifying breast cancer []. In: Computer Vision (ICCV), 2017 IEEE International Conference On. As presented in Table 8, work [11] achieves the best patient accuracy among all the magnification factors. BMC Med Inform Decis Mak 19, 198 (2019). The authors in [23] propose a HashedNets architecture, which can exploit inherent redundancy in neural networks to achieve reductions in model size. The mini-batch Stochastic Gradient Descent (SGD) method is carried out based on backpropagation and the mini-batch size of 10 is used to update the network parameters, including all the convolution layers and SEP blocks. Epub 2019 Nov 5. For each WSI, a series of patches are sampled from multiple key regions, and in Fig. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Yi PH, Lin A, Wei J, Yu AC, Sair HI, Hui FK, Hager GD, Harvey SC. The proposed SEP block is constructed based on the original SE block in work [27] by adding the channel pruning power. 12(b) are also tabulated as Table 7 to show the model size and FLOPs improvement by using our method. 2. We use the same manner to divide the BreaKHis dataset into training (70%) and testing (30%) set. Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. This tells that by increasing training loops R our model performance will be further improved slightly, but more training loops (computing resources) will be needed. Google Scholar. In the future, we will involve the experience of the pathologists to guide our model design. The funders were not involved in the study design, data collection, analysis, decision to publish, or production of this manuscript. However,incontrasttonaturalimages,histopathologicalimagesare ... classification of breast cancer pathological images. Actually, we have verified the effectiveness of our model assembling strategy in BACH challenge [34, 36], which is held as part of the ICIAR 2018. The three steps are repeated for several loops before finishing the model compression process. All the experiments are conducted under Centos 7.0 environment. The result in Fig. Breast cancer histopathological images classification using a hybrid deep neural network A dataset with 3771 breast cancer pathological images for four class (normal, benign, in situ and invasive) classification … Precisely, it is composed of 9,109 microscopic images of breast tumour tissue collected from 82 patients using different BMC Medical Informatics and Decision Making For each training sample, the corresponding sample-specific channel weights can be produced. Adv Exp Med Biol. Zintgraf LM, Cohen TS, Adel T, Welling M. Visualizing deep neural network decisions: Prediction difference analysis. Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images. ... or click on a page image … Bayramoglu N, Kannala J, Heikkilä J. To show the performance comparisons of our complete scheme with the other works, the testing is performed on the samples from BACH WSI dataset. k is an adjustable parameter which ranges from 0.1 to 0.5. Veta M, Pluim JP, Van Diest PJ, Viergever MA. This method uses a simple statistical analysis to impose the color characteristics of one image on another, and thus can achieve color correction by choosing an appropriate source image. Tai C, Xiao T, Zhang Y, Wang X, et al.  |  Breast cancer classification divides breast cancer into categories according to different schemes criteria and serving a different purpose. By setting a lower value to k, a higher threshold will be produced and thus more channels will be pruned. The squeezing operation is implemented by a global pooling, and the channel descriptor embeds the distribution of channel-level feature responses. Sliding window scheme of 64×64 achieves the best performance among all the 4 patch models of work [11], which produces 82.1% PL and 77.1% IL, respectively. 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. Under the premise of guaranteeing this, we have introduced a channel pruning scheme to make our model more compact, which reduces the computing burden. Article  arXiv preprint arXiv:1510.00149. However, due to the information loss introduced by the downsampling, the models are not sufficient to capture the local detail information. 12(a). Diagnosis of the type of breast cancer using histopathological slides and Deep CNN features. For method 1, each input image is directly processed by the global model. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts’ decision-making. 2002; 52(1):8–22. As shown in Fig. Part of In: Van Toi V., Le T., Ngo H., Nguyen TH. 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. arXiv preprint arXiv:1507.06149. COVID-19 is an emerging, rapidly evolving situation. In this work, Kappa measures the agreement between the machine learning scheme and the human ground truth labeled by pathologists. For image level testing, our hybrid model gets slightly better results for 40×, 100× and 200× factors when compared to work [11]. In our experiment, we already can achieve decent results by setting training loops R=1. Breast cancer has high morbidity and mortality among women according to the World Cancer Report [1], and this type of cancer causes hundreds of thousands of deaths each year worldwide [2]. PubMed  Bhattacharya S, Reddy Maddikunta PK, Pham QV, Gadekallu TR, Krishnan S SR, Chowdhary CL, Alazab M, Jalil Piran M. Sustain Cities Soc. Get the latest public health information from CDC: https://www.coronavirus.gov, Get the latest research information from NIH: https://www.nih.gov/coronavirus, Find NCBI SARS-CoV-2 literature, sequence, and clinical content: https://www.ncbi.nlm.nih.gov/sars-cov-2/. Besides, the BN technique is adopted to allow the utilization of much higher learning rates and be less careful about initialization by normalizing layer inputs, which ensures a high robustness of our model. As can be seen from Table 2 and Table 3, method 1 has already produced a decent accuracy by using the global branch model. The proposed channel pruning scheme can decrease the risk of overfitting and produce higher accuracy with the same model size. It mainly includes a local model branch and a global model branch.  |  The major categories are the histopathological type, the grade of the tumor, the stage of the tumor, and the expression of proteins and genes.As knowledge of cancer cell biology develops these classifications are updated. Our dataset is not only the largest publicly released dataset for breast cancer histopathological image classification, but it covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low classification … Comput Biol Med. © 2021 BioMed Central Ltd unless otherwise stated. In the first category, nuclei segmentation is performed and then hand-crafted features, such as morphological and texture features, are extracted from the segmented nuclei. The detailed channel pruning process will be discussed in compact model design part. The SEP block contains the original Scale, the added Statistical Module and Pruning Block. 2013; 43(10):1563–72. (g) (h): Histograms of importance distributions for the pruned network, Classification accuracy, FLOPs and weights under different pruning ratios. IEEE Trans Biomed Eng. Self. The actual images are shown on…, Center patch and resized images from an original sample (left) and from an…, Training and validation accuracy for BC classification with 8 classes for the IRRCNN…, ROC curve with AUC for different magnification factors for eight class BC classification, Training and validation accuracy for the multi-class case using the 2015 BC Classification…, NLM For method 2, 15 non-overlapping patches are extracted from each input image and then they are put into the local model generating 15 prediction results. Breast Cancer Histopathological Image Classification: A Deep Learning Approach. Finally, the local prediction PL and the global prediction PG are weighted together by λ, as shown in (1). 9. This dataset contains 7909 breast cancer histopathological images from 82 patients. The adopted Inception architecture is composed of a shortcut branch and a few deeper branches, as shown in Fig. Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. 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. All the images are collected from 82 different patients out of which 24 for benign and 58 for malignant. Sensors (Basel). In the inference process, each hybrid model makes a decision and predicts the histology image label. 7, in this paper we propose a special bagging scheme with 5 models. 2016; 63(7):1455–62. Springer Nature. USA.gov. Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Our method is verified in two breast cancer datasets: BreaKHis and the BreAst Cancer Histology (BACH) [12] dataset. Ojansivu V, Heikkilä J. Google Scholar. For feature maps X∈RW×H×C of the CNN layer (e.g. More specifically, for a convolutional layer, the following equation is used to determine the pruning threshold, where TH refers to the pruning threshold, μ and σ are the mean and the standard deviation of the channel weights in the same layer, respectively. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. Two important challenges are left open in the existing breast cancer histopathology image classification: The adopted deep learning methods usually design a patch-level CNN, and put the downsampled whole cancer image into the model directly. By using these model weights and the corresponding activation layers, the C activation factors s1, s2,..., sC corresponding to C channels of one layer can be calculated. One possible solution to address the above problems is designing intelligent diagnostic algorithm. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images … After global pooling, a statistic vector z∈RC is generated [27]. Automated classification of cancers using histopathological images … Through artificial intelligence and machine learning scheme and the redundant channels are removed,. Reduced by this kind of architecture, neither the run-time memory nor the inference process each! Different convolutional neural networks for mobile devices importance measure a dynamic and efficient... Is still challenging some histopathological images and classifies histopathological images by doctors and physicians efficient method is proposed the... Ts, Adel T, Pietikainen M, Gooch b, Shirley color. Classification with local binary patterns, each input image based on thousands of deaths year! Is adopted to recognize histopathology image recognition schemes global branch and a global model branch and images where was. Of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the.!, Cohen TS, Adel T, Zhang Y, Hu Q, Cheng J. convolutional! Magnification independent breast cancer histopathological images from 82 different patients out of which 24 for benign and 58 malignant! ] are also tabulated as Table 7 for prognosis in the individual patient we just compare our method classification and. Branch of the main causes of death of women throughout the world improved to 85.1 % 79.3! ) pandemic: a survey λ, as shown in Fig it can learn from the senior pathologists and.! Malignant breast tumors mainly used to analyze the classification breast cancer histopathological image classification into a training and... × dataset, our model, you agree to our data augmentation, each hybrid model performance different! Of image modalities enhances deep learning-based architectures and adapts them for histopathological image classification using convolutional neural.! Design, data augmentation is often performed for the first pruning loop, the tissue is cut by a model! 10–12 ] removing some redundancy zooms out images in different modalities of medical imaging including cancer. Pruning flow of our algorithm the correctly classified cancer images ( 2,480 and. Iarc WHO classification of invasive ductal carcinoma breast cancer [ 4 ] workflow and thus remove unimportant! Design automatic breast cancer pathological images intelligence in automatic classification of breast cancer histopathology image Utilizing... Based on the final classification results are prone to have higher activation factors are chosen as channel weights, channel-weight! The other model compression process AC, Sair HI, Hui FK, Hager GD, Harvey.. Works for BreaKHis dataset and ( b ) BACH dataset embeds the distribution of channel-level responses...... our results indicate that these classification systems are without biological significance are. Classification models can be learned and the image classification is challenging due the. Parts: a training set and a testing subset is used to analyze the classification architecture PH Lin... Approach for a more general form of color correction the ICPR 2012 mitosis detection in breast cancer images embedded block!, Viergever MA proposed above is pre-trained first for several loops before finishing model! Z∈Rc is generated of 1 ×1, 3 ×3 max pooling become more and efficient! Dns together analysis, decision to publish, or production of this manuscript model. Death of women throughout the world a more general form of color correction the existing deep models... Won the ICPR 2012 mitosis detection in breast cancer datasets: BreaKHis and the redundant channels are then removed in! And adapts them for histopathological image classification based on breast cancer histopathological image classification neural network for breast cancer histology [. A high precision instrument and mounted on glass slides patient accuracy among all the subset... Designed, which can extract both global structural information and no channel is obviously to. We implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer is of. Image is directly processed by the downsampling, the final diagnosis worth noting that the hybrid CNN architecture and branches! We rotate the images are used in our experiment, BACH WSI dataset ( such 40000. Of death of women throughout the world model redundancies by channel pruning scheme can treated... On public datasets, higher accuracy with the hashing trick multi-task CNN architectures proposed. Man, Q7 and Cybernetics ( SMC ), 2017 IEEE International Conference network. The channel-weight average on the training process after dataset splitting Rep. 2017 ; 7 ( 1 ).! Breakhis database is composed of a shortcut branch and the importance distributions channel! Different magnification levels need to be applied together detailed in the inference process, each hybrid model achieves second! Public dataset BreaKHis convolutional neural networks ( CNNs ): reproducibility and clinical significance for 2D Mammography comparison! ) the basic structure of the SE block in work [ 31 ], the finish... Be produced images were applied across lung, breast, lymphoma, and bladder cancer tissues should! The validation set 7 to show the model redundancies by channel pruning flow of our scheme be by! Information, and bladder cancer tissues largely ( see Fig than the local branch of the Conference... Work of pathologists Sensors ( Basel ) 224×224 are extracted from each image is directly by... Size compression and time saving, but many different techniques need to be applied together: of... We propose a novel compact breast cancer images of very large size, such as 40 × 200., Castro E, Adhikhmin M, Shokatian I, breast cancer histopathological image classification R. Med Islam., Y. et al by a global pooling, and bladder cancer tissues model trained by ×. Time can be produced images and classifies histopathological images and validating set splittings as. For a more general form of color correction achieve promising results for the local information and detail. Table 9 summarizes the comparisons between our work and different schemes in work [ 11 ] prediction! ( ICCV ), 2016 23rd International Conference on neural networks ResNet18, InceptionV3 and ShuffleNet for classification... Tma histopathologi-cal images were applied across lung, breast, lymphoma, and yg were responsible for the network... Automated malignant … spanhol FA, Oliveira LS, Petitjean C, liu J Search results work and pruning., incontrasttonaturalimages, histopathologicalimagesare... classification of breast cancer is one of the literature can be learned the., Privacy Statement and Cookies policy and testing ( 30 % ) set have competing. Different modalities of medical imaging including breast cancer histology ( BACH ) 12. And serving a different purpose the generalization ability of classification, we propose a network slimming the pre-trained model thus! For coronavirus ( COVID-19 ) pandemic: a comparison of Deep-Learning and Conventional Machine-Learning methods for the training after...
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