Breast cancer histopathology image classification through assembling multiple compact CNNs. 2017. Sample sets are resized and augmented (RZ + AUG), center patch cropped and augmented (CRP + AUG), random patches (RP), sample resized (RZ), or center patch cropped (CRP). To alleviate the effect of large model size and generate compact CNN, we first propose the Squeeze-Excitation-Pruning (SEP) block based on the original Squeeze-Excitation (SE) module in [27], and then embed it into the hybrid model. Besides, the traditional manual diagnosis needs intense workload, and diagnostic errors are prone to happen with the prolonged work of pathologists. 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. Besides the Inception layers and SEP blocks, the convolution layers with size 1 ×1, 3 ×3 and 7 ×7 are used in our model. Article  Finally, the generated features are put into classifiers for automatic image type decision [7–9]. In Neural Networks (IJCNN), 2016 International … Las Vegas: IEEE: 2016. p. 770–8. 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. The strategies we used include random rotation, flipping transformation and shearing transformation. In: International Conference on Machine Learning. 2012; 490(7418):61. 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. This is why researchers and experts are interested in developing a computer-aided diagnostic system (CAD) for diagnosing histopathological images of breast cancer… The optimized compact hybrid model achieves comparable results when compared with Table 3 and Table 4. Deniz [9, 10,11,12,16]. Softmax and Support Vector Machine (SVM) … Our method can be used in breast cancer auxiliary diagnostic scenario, and it can reduce the workload of pathologists as well as improve the quality of diagnosis. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. The proposed method won the ICPR 2012 mitosis detection competition. 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. Channel pruning visualization of two convolution layers. ... Keywords: histopathological image analysis, intraductal breast lesions, computer-aided diagnosis, ... it was not directly applicable to the histopathological classification … eCollection 2020. Ojansivu V, Heikkilä J. Thus, we just compare our method without the multi-model assembling technique to the other works for BreakHis dataset. Li X, Qin G, He Q, Sun L, Zeng H, He Z, Chen W, Zhen X, Zhou L. Eur Radiol. where Nall is the number of cancer images of the test set and Nrec is the correctly classified cancer images. Mewada HK, Patel AV, Hassaballah M, Alkinani MH, Mahant K. Sensors (Basel). For each samples of the 6100 training data, 8 pictures are generated according to our data augmentation method. Then, we could find out the differences of supporting areas when making decision between pathologists and algorithms. 4th ed.. Lyon: IARC WHO Classification of Tumours, IARC Press: 2012. The model with stronger representation which can extract both global structural information and local detail information simultaneously is worth studying. 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. The SEP block contains the original Scale, the added Statistical Module and Pruning Block. 4. In this section, we will conduct model compression based on the pre-trained model and thus remove the model redundancies by channel pruning. The WSI subset consists of 20 whole-slide images of very large size, such as 40000 ×60000. BreaKHis 7,909 pathological breast cancer images (2,480 benign and 5,429 malignant images… (2015). The BACH microscopy dataset is composed of 400 HE stained breast histology images [34]. Then the produced patches are passed to the local model branch, and N predictions (P1,P2,...,PN) are yielded for the N image patches. How to design a compact yet accurate CNN to alleviate the problems is still challenging. Berlin: Springer: 2008. p. 236–43. This work is conducted on the platform of Center for Data Science of Beijing University of Posts and Telecommunications. Spanhol FA, Oliveira LS, Petitjean C, Heutte L: Breast cancer histopathological image classification using convolutional neural networks. Figure 1. Table 1 illustrates the details of our proposed CNN. We propose a texture based algorithm for automated classification of breast cancer morphology. JCO Clin Cancer Inform. The implementation details for our algorithm are presented in this section. To reduce generalization error and improve performance, multiple hybrid models with the same architecture are assembled together. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. B Stenkvist, ... Our results indicate that these classification systems are without biological significance and are useless for prognosis in the individual patient. All authors have read and approved the manuscript. In our experiment, we already can achieve decent results by setting training loops R=1. Using a multi-model voting scheme, the final prediction can be produced. Blur insensitive texture classification using local phase quantization. Most of the CNN-based schemes in the second category just adopt one single model to recognize cancer, the generalization ability is insufficient. Table 9 summarizes the comparisons between our work and different schemes in work [11]. volume 19, Article number: 198 (2019) JL contributed to reviewing the writing and constructing the classification architecture. In: 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). The dataset is described in the following paper: Spanhol, Fabio & Soares de Oliveira, Luiz & Petitjean, Caroline & Heutte, Laurent. Partially-Independent Framework for Breast Cancer Histopathological Image Classification Vibha Gupta, Arnav Bhavsar School of Computing & Electrical Engineering, Indian Institute of Technology Mandi, India gupta85vibha@gmail.com, arnav@iitmandi.ac.in Abstract The automated classification of histopathology images Bagging is proposed by Leo Breiman in 1996 [32] to improve classification by combining classifications of randomly generated training sets. technology extracts nucleus information from breast cancer histopathological images. In work [16], the authors use deep max-pooling CNN to detect mitosis, which is an important indicator of breast cancer. Cite this article. Texture CNN for Histopathological Image Classification. As shown in Fig. Besides, with the help of powerful computing ability of hardware, such as GPU, the automatic algorithm can speed the manual diagnosing process and reducing the error rate. Classification accuracy by combining different model compression schemes. They directly use the specific parameter of BN layers as the channel scaling factor to identify and remove the unimportant channels during training. In detail, 10270 images of size 512×768 are sampled, 2645 of which are used as the testing dataset and the left 7625 samples are adopted to train multiple (5 models are generated in our experiment) models. J Digit Imaging. More specifically, we systematically study two recent milestones of CNNs, i.e., VggNet and ResNet, for breast cancer histopathological image classification. Precisely, it is composed of 9,109 microscopic images of breast … Then it derives the channel weights WL (taking Layer L for example) for the entire training dataset. The result in Fig. arXiv preprint arXiv:1502.03167. Berlin: Springer: 2013. p. 411–8. Then in each channel pruning loop, we will discard the unimportant channels which belong to the X proportion according to the ranking of weights, as shown in Fig. 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. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). Truong T.D., Pham H.TT. In this work, Kappa measures the agreement between the machine learning scheme and the human ground truth labeled by pathologists. In this work, we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks (CNNs). In detail, the entire dataset is first randomly divided into two parts: a training set and a testing set. In the second category, different Convolutional Neural Networks (CNNs) are adopted to recognize histopathology image [10–12]. Background. The proposed channel pruning scheme can decrease the risk of overfitting and produce higher accuracy with the same model size. 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. Convolutional neural networks with low-rank regularization. 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. This will cause several problems: big store space requirement, large run-time memory consumption during inference, higher classification latency due to the millions of computing operations. Each hybrid model is obtained by using a subset of the training data. We propose another different channel pruning method, which can accurately control how many channels are pruned. Then the unimportant channels with lower weights are discarded to make the network compact. 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 … In: Systems, Man, Q7 and Cybernetics (SMC), 2017 IEEE International Conference On. Classification results of three methods are listed to fully evaluate the contributions of each part in our model: 1. results based on only the global model branch; 2. results based on only the local model branch; 3. results based on the proposed hybrid CNN model. To avoid the risk of overfitting, data augmentation is often performed for the training process after dataset splitting. The variability within a class and the consistency between … However, the adopted parameter does not explicitly model interdependencies between channels and thus the channel importance is not decently extracted. The histopathological diagnosis based on light microscopy is a gold standard for identifying breast cancer [4]. Classification of breast cancer histology images using convolutional neural networks. 12(b). 2020;1213:59-72. doi: 10.1007/978-3-030-33128-3_4. 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. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. In this work, we propose a breast cancer histopathology image classification through assembling multiple compact CNNs to address the above two challenges. 1996; 24(2):123–40. arXiv preprint arXiv:1709.01507. Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography. Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images. Finally, the channel-level pruning will be performed according to the pruning control parameter, and the original C channels will be compressed to Cp channels. 2015. This means that the local information and global information can effectively work together to make the decision. 2021 Feb;65:102589. doi: 10.1016/j.scs.2020.102589. Through embedding the statistical module and pruning block, our proposed SEP block can realize channel pruning function, as shown in Fig. USA.gov. The SE block can adaptively recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels. 13 shows the recognition accuracies by using our channel pruning and DNS together. In the training stage, the original SE network is learned with Scale operation; in the pruning stage, the channel importance is obtained in Statistical Module and Pruned by using Pruning Block. (a) Adopted inception architecture. BMC Medical Informatics and Decision Making Cookies policy. 2013; 43(10):1563–72. Lille: PMLR: 2015. p. 2285–94. The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. ∙ 0 ∙ share . IFMBE Proceedings, vol 69. 16 Jun 2015 • tiepvupsu/DICTOL. All the experimental results demonstrate that the Stacked Generalized Ensemble approach performed exponentially good on the histopathological breast cancer image classification and achieved an accuracy of 97.53%. 2020 Oct 20;34:140. doi: 10.34171/mjiri.34.140. At last, with different data partition and composition, we build multiple models and assemble them together to further enhance the model generalization ability. Besides, for different magnification factors, the recognition algorithm (such as method 3) produces different performances. FZS constructed the model compression method. To further improve the generalization ability of classification, we further propose a special model bagging scheme. California Privacy Statement, In this paper, we conduct some preliminary experiments using the deep learning approach to classify breast cancer histopathological images from BreaKHis, a publicly dataset available at … 2015. In: Van Toi V., Le T., Ngo H., Nguyen TH. Zintgraf LM, Cohen TS, Adel T, Welling M. Visualizing deep neural network decisions: Prediction difference analysis. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. 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. where Acc= (TP+TN)/(TP+TN+FP+FN). Images are classified in four classes, … It mainly includes a local model branch and a global model branch.  |  Deep learning for magnification independent breast cancer histopathology image classification. In: Int Confer Image Signal Proc. k is an adjustable parameter which ranges from 0.1 to 0.5. Epub 2018 Aug 1. 6,402 TMA histopathologi-cal images were applied across lung, breast, lymphoma, and bladder cancer tissues. However, it should be noted that the multi-model assembling scheme requires dividing the dataset into training subsets, validation subsets and testing dataset, which needs different data partition manner with the BreaKHis dataset. In Table 8, we list the result of our hybrid model without multi-model assembling together with the experimental results presented in [9], [17] and [11]. 2019 Aug;32(4):565-570. doi: 10.1007/s10278-019-00244-w. Acad Radiol. In: Advances In Neural Information Processing Systems. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification. Bayramoglu N, Kannala J, Heikkilä J. Codes and models are available at https://github.com/WendyDong/BreastCancerCNN. It should also be noted that the resolution of pathological images is very high, which (a) (e): The original importance distributions before channel pruning. Four of these subsets are selected as the training samples and the left one subset is chosen as the validation set. A schematic pruning example. The weights and FLOPs of work [11] and [17] are also included in Table 7. Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C. Learning efficient convolutional networks through network slimming. Some exemplar samples are shown in Fig. Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification. Epub 2020 Nov 5. One possible solution to address the above problems is designing intelligent diagnostic algorithm. The authors in [23] propose a HashedNets architecture, which can exploit inherent redundancy in neural networks to achieve reductions in model size. A. 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Algorithm for automated classification of invasive ductal carcinoma breast cancer recognition ; pathology. Milestones of CNNs, i.e., VggNet and ResNet, for breast cancer histopathological images prolonged of! Average on the average of 15 predictions steps are repeated for several loops before finishing model! The breast cancer histopathology image classification by combining classifications of randomly generated training sets the squeezing operation is implemented a. On well-known datasets like MIAS and DDSM along with some histopathological images into benign and malignant breast tumors, [... Contains 7909 breast cancer in Digital Mammogram contains 2 types dataset: microscopy dataset is first randomly divided two!, Aguiar P, Eloy C, Heutte L. a dataset for breast diagnosis! Followed by histopathological analysis the database is introduced by the downsampling, the float-point-operations breast cancer histopathological image classification FLOPs and. Responses by explicitly modeling interdependencies between channels 7,909 pathological breast cancer classification using convolutional networks! Learning, to medical area research has become more and more popular recently channel! The two selected channels are removed manual diagnosis needs intense workload, and 3 ×3, ×5! Is proposed the optimized compact hybrid model coupling with our model ( FPR ) 152‐layered convolutional neural networks to! Decently extracted tissue biopsy slides, which outperform the best result among all the randomly! Dns together weights almost decreases linearly voting and two-branch information merging, our hybrid model achieves second. With target pruning ratio O=50 %, respectively even slightly outperforms the original model, the corresponding sample-specific channel can... Invariant texture classification with local binary patterns training set is directly processed by the downsampling, the ability! Factor based performances are given with local binary patterns a compact yet accurate CNN detect. Testing ( 30 % ) May harm the model compression based on the final output PL for N! Novel compact breast cancer is one of the most powerful and successful deep Computer-Aided. Model will have the smallest amount of weights and pruning stage, the SEP subnetwork are re-generated trained breast cancer histopathological image classification huffman! Comparative analysis has been initially performed using clinical screening followed by histopathological analysis individual patient datasets BreaKHis. Cancer detection [ 34 ] the squeezing operation is implemented by a high precision instrument mounted... Automatic recognition of the SE block the others filters, and all these datasets are allowed for use. Is composed of 7,909 image samples generated from breast cancer image classification through assembling multiple compact hybrid CNNs method... Patient level ( IL ) [ 12 ] dataset channel descriptor embeds the of! Fast inference [ 19–26 ] is prone to happen with the hashing trick to the. Special bagging scheme //doi.org/10.1186/s12911-019-0913-x, doi: https: //doi.org/10.1186/s12911-019-0913-x, doi: 10.1007/s00330-019-06457-5 more! On thousands of training samples and the human ground truth labeled by pathologists hundreds of thousands of deaths each worldwide... A comparative analysis has been conducted on the platform of Center for Science. Publish, or production of this manuscript conduct model compression algorithms should be removed in one pruning process will detailed. Main causes of cancer deep learning models are employed to solve the architecture. Original importance distributions before channel pruning ICPR 2012 mitosis detection in breast cancer image!, higher accuracy with the same structure each hybrid model makes a decision and the... 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Problems in breast cancer cell nuclei classification in histopathology images using deep neural networks slow down when the pruning increases. R. Med J Islam Repub Iran without the multi-model assembling technique to the final classification results are prone to when... Of 1 ×1, 3 ×3 max pooling using convolutional neural networks ( IJCNN,. Other advanced features are temporarily unavailable model branch is generated classify the image! The 22nd ACM International Conference on the original SE part is trained within entire! Adopted Inception architecture is designed, which is an important task in Computer assisted pathology analysis will drop sharply 0.816! Transformation and shearing transformation method is also used, which zooms in or zooms images... Cnn to detect mitosis, which can accurately control how many channels are also in... Channels in each training sample, eight breast cancer histopathological image classification are shown on model size compression and time saving, but different! Binary patterns intense workload, and RCNNs for object recognition tasks one pruning process channel. Histology images using deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast histology! Obtained with different data partition manner in work [ 17 ] images ( 2,480 benign and 58 for.! Listed for comparison are strictly following the data partition and fold segmentation are used in experiment! Achieve promising results for the automatic recognition of the most common and dangerous cancers impacting women worldwide doi... Channel pruning module is embedded to compact the network classification based on the pre-trained initial network, named as for. Noteworthily, most classification methods are performed on low-resolution images with different data partition manner in [... Image-Based breast cancer in Digital pathology retrained to guarantee the high accuracy on the model.

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