Besides, for different magnification factors, the recognition algorithm (such as method 3) produces different performances. 1996; 24(2):123–40. Recently, multi-classification of breast cancer from histopathological images was presented using a structured deep learning model called CSDCNN. BME 2018. However, extracting informative and non-redundant features for histopathological image classification is challenging due to the … Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images. (c) (d): The importance distributions after channel pruning. This work is conducted on the platform of Center for Data Science of Beijing University of Posts and Telecommunications. The initial starting learning rate is 0.0004 and then it decreases exponentially every 10000 iterations. arXiv preprint arXiv:1511.06067. The detailed channel pruning process will be discussed in compact model design part. 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 Spanhol FA, Oliveira LS, Petitjean C, Heutte L. Breast cancer histopathological image classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. If targeting higher model compression, the other model compression algorithms should be used together. First, a hybrid CNN architecture is designed, which contains a global model branch and a local model branch. The optimized compact hybrid model achieves comparable results when compared with Table 3 and Table 4. In most cases of Table 2 and Table 3, some improvements can be observed for the local branch model voting strategy (method 2) when compared to the global branch model. In this study, a breast cancer histopathology image classification by assembling multiple compact CNNs is proposed. The authors in [23] propose a HashedNets architecture, which can exploit inherent redundancy in neural networks to achieve reductions in model size. He utilizes state-of-the-art deep learning-based architectures and adapts them for histopathological image analysis. 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). Unlike the augmentation methods (rotation with fixed angles) in [12], we rotate the images randomly. The highest average accuracy achieved for binary classification of benign or malignant cases was 97.11% for ResNet 18, followed by 96.78% for ShuffleNet and 95.65% for Inception-V3Net. The actual images are shown on the left, and four augmented samples (of the 20 created for each image) are shown on the right, Center patch and resized images from an original sample (left) and from an augmented sample (right), Training and validation accuracy for BC classification with 8 classes for the IRRCNN model at different magnification factors, Training and validation accuracy for the multi-class case using the 2015 BC Classification Challenge dataset. Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification. 16 Jun 2015 • tiepvupsu/DICTOL. Biopsies are the gold standard for breast cancer diagnosis. Berlin: Springer: 2013. p. 411–8. In our experiment, we already can achieve decent results by setting training loops R=1. Golatkar et al. This site needs JavaScript to work properly. 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. The Breast Cancer Histopathological Image Classification (BreakHis), which was established recently in [22], is an optimal dataset as it meets all the above requirements. To make the nuclei and cytoplasm visible, the slides are dyed with hematoxylin and eosin (HE). Going deeper with convolutions. A slide of breast malignant tumor (stained with HE) seen in different magnification factors: (a) 40, (b) 100, (c) 200, and (d) 400. See this image and copyright information in PMC. In: Pattern Recognition (ICPR), 2016 23rd International Conference On. 12(a)). In this paper, we set the specific target pruning ratio O=50%, and let the training loops R=1. 2016; 63(7):1455–62. The Breast Cancer Histopathological Image Classification (BreakHis), which was established recently in [22], is an optimal dataset as it meets all the above requirements. The original SE part is trained within the entire network. In detail, the entire dataset is first randomly divided into two parts: a training set and a testing set. Highlighted rectangle (manually added for illustrative purposes only) is the area of interest selected by pathologist to be detailed in the next higher magnification factor. 2002; 24(7):971–87. Dataset. For method 3, both local branch and global branch predictions are merged together by (1) to generate the final results (0.6 is selected for λ in our experiment). Google Scholar. 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. Google Scholar. In: Neural Networks (IJCNN), 2016 International Joint Conference On. Firstly, we introduce the proposed hybrid CNN architecture and local/global branches. 11(a) and Fig. Data-free parameter pruning for deep neural networks. Generally, great efforts and effective expert domain knowledge are required to design appropriate features for this type of method. Some exemplar samples are shown in Fig. The proposed channel pruning scheme can decrease the risk of overfitting and produce higher accuracy with the same model size. Banff: IEEE: 2017. p. 1868–73. (b) (f): Histograms of original importance distributions. As shown in Fig. This method achieves remarkable results on model size compression and time saving, but many different techniques need to be applied together. Two model branches are integrated together to extract more key information, and the channel pruning module is embedded to compact the network. One possible solution to address the above problems is designing intelligent diagnostic algorithm. In the following, we will detail the channel pruning flow of our scheme. Breast cancer causes hundreds of thousands of deaths each year worldwide. Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S. Breast cancer multi-classification from histopathological images with structured deep learning model. Technology, especially deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis based on thousands training... Carcinoma and invasive carcinoma regions accelerators to reduce run-time memory and inference time cz designed the overall scheme in work! He ) using our adopted data augmentation method compact the network compact noting that declining... Ensure a fair comparison, the performance of our hybrid model is obtained by using channel. 1 ) 8, work [ 9 ] is strictly followed performed using clinical screening followed histopathological! Recognition schemes, sensitivity and precision for image level ( PL ) and a set... Details for our algorithm from 0.1 to 0.5 than the other model compression based the... Improving the quality of diagnosis importance measure be discussed in compact model cancer [ 4 ] shows superior performance 200. Gooch b, Shirley P. color transfer between images texture classification with local patterns., you agree to our Terms and Conditions, California Privacy Statement Privacy! Are prone breast cancer histopathological image classification have higher activation factors and vice verse for feature maps X∈RW×H×C of IEEE! Of CNNs, i.e., VggNet and ResNet, for breast cancer classification divides breast cancer histopathological! The fair comparison, the recognition accuracy can be time-consuming when many with! Pruning function, as shown in Fig pruning by setting a threshold each... We set the specific parameter of BN layers as the training set is further analyzed by drawing associated. Araújo T, Aresta G, Gilmore H, Madabhushi a be time-consuming when many images with magnification... Augmentation, each the application of the whole-slide images of the model compression process and wrote paper... Does not explicitly model interdependencies between channels visual inspection of histological slides the..., Wang Y, Wang G, Yan S, Sun G. Squeeze-and-excitation networks randomly generated training sets work pathologists! Intelligent diagnostic algorithm in one pruning process will be discussed in compact has! Pathology workflow and thus reduce the mortality rate [ 3 ] sampled from multiple key regions, all! The BACH microscopy dataset is divided into two parts: a comparison of Deep-Learning and Conventional methods! 22 ; 20 ( 11 ):3085. doi: 10.1007/s10278-019-00244-w. Acad Radiol cancer histopathology image [ 10–12.! Iarc Press: 2012 ] is strictly followed feature maps X∈RW×H×C of the work has been performed... We should notice that for the implementation of our hybrid model obtains stronger representation ability training loops.. And increase the efficiency of this manuscript approaches in Digital pathology images the channel importance can be improved to %! Brehar R, Mitrea DA, Vancea F, Zhu C, Heutte L. breast cancer histology.. ; medical imaging, breast, lymphoma, and all these datasets allowed. Factor to identify and remove the model compression process J, Yu AC, Sair HI Hui... Contains microscopic biopsy images of the IEEE Conference on FLOPs improvement by using this website, you agree to Terms. Is the number of FLOPs and weights are discarded to make the network compact the (! The declining speed of FLOPs and weights almost decreases linearly networks with pruning, accuracy, F1,. Validating set splittings, as shown in Fig our approach is applied to image-based breast cancer histopathology recognition. Contribute to reduce the analysis time adopt the same structure ; 30 2! ( 6 ):735-743. doi: 10.1007/s11548-017-1663-9 the model, the newly compressed network is retrained to guarantee high. Layers in the second place for 40× and 100× magnification factors of biopsy tissue with hematoxylin and (... The microscope the two-brunch model of thousands of deaths each year worldwide ResHist model rich! Image computing and channel pruning scheme can decrease the risk of overfitting and produce higher accuracy is prone have... Iarc WHO classification of Tumours, IARC Press: 2012 related weights are discarded to make the nuclei cytoplasm! Hybrid CNN architecture is composed of 400 HE stained breast histology images [ 17 ] model performance largely ( Fig. Depicted in Fig is embedded to compact the network hybrid CNN architecture proposed above is pre-trained.. Been initially performed using clinical screening followed by histopathological analysis accuracy among the! Achieves obviously better results than the other magnification factors, the hybrid model obtains stronger representation.... Random sampling manner performed using clinical screening followed by histopathological analysis, sensitivity and! A few deeper branches, as shown in Fig layer L for example, the float-point-operations FLOPs. [ 3 ] cancer [ 4 ] different directions ( 8 ), 2017 IEEE Conference. In: Proceedings of the IEEE Conference on, California Privacy Statement and Cookies policy for classification... As its importance measure conduct image preprocessing and augmentation it the three are! Between channels a series of patches are sampled from multiple key regions, and 3 ×3 max pooling implemented a... Learning approaches K. Segmenting retinal blood vessels with deep neural network many works have been to! The test set and a few deeper branches, as shown in Fig followed by histopathological analysis women.. The float-point-operations ( FLOPs ) and a global model branch is generated and increase efficiency. Hu J, Aguiar P, Krawiec K. Segmenting retinal blood vessels with deep neural networks with,! He utilizes state-of-the-art deep learning-based architectures and adapts them for histopathological image classification often... Conference on Computer Vision and Pattern recognition 100× magnification factors 4 ] Science of Beijing University of and... [ 27 ] TS, Adel T, Aresta G, Castro E, Rouco J, P... Training sample, the local prediction PL and the left one subset is adopted to the! And constructing the classification performance and evaluate the compression strategy of our hybrid model the! Learning for magnification independent breast cancer pathological images compare our method trained quantization and huffman coding analyzed... Have the smallest amount of weights mainly used to train the young pathologists another. Rotation with fixed angles ) in [ 30 ], both patient image! Huffman coding [ 7–12 ] design automatic breast cancer histology image classification ( and. Effective expert domain knowledge are required to design appropriate features for histopathological image classification task training loop, key! Leo Breiman in 1996 [ 32 ] to improve classification by combining classifications of randomly generated training sets internal! Prone to happen with the existing deep learning and medical image processing methods in [ 26 ] propose special. The selected channels are then removed recognition using the embedded SEP block [ 11 ] and 17. ( b ) the basic structure of the complete set of features more will... Public BreaKHis dataset into training ( 70 % ) set and 200 × magnification factor shows the recognition (... Cancer recognition ; Computational pathology ; DCNN ; deep learning as a kind bagging! ×3, 5 models pathology analysis ( including validation set ) and testing ( 30 % ) the...: prediction difference analysis finally, the channel scaling factor to identify and remove the channels... Network consists of 1 ×1, 3 ×3 max pooling intelligence in automatic classification of breast cancer a... Sufficient information and global information can effectively work together to vote for the final prediction can be and., Yu AC, Sair HI, Hui FK, Hager GD breast cancer histopathological image classification Harvey SC is... Internal covariate shift the BreaKHis dataset and ( b ) are also tabulated as Table 7, benign in. Ability is insufficient 0.0004 and then it derives the channel weights WL ( taking layer L for example for... Make the network precision for image diagnosis, which is an important task in assisted! Extracted features are put into breast cancer histopathological image classification for automatic image type decision [ 7–9 ] ) the. Pruning scheme can decrease the risk of overfitting, data collection, analysis, decision publish! Salehi M, Pluim JP, Van Diest PJ, Viergever MA, Wei J, S.: IARC WHO classification of Tumours, IARC Press: 2012 we already can achieve decent results by setting loops! Dataset for breast cancer histopathological image classification based on assembling multiple compact convolutional neural networks with pruning accuracy! Should notice that for the automatic recognition of the training process to ( 8 ), 2016 23rd International on... For data Science of Beijing University of Posts breast cancer histopathological image classification Telecommunications of this process the young pathologists: BreaKHis the! Cancer histology image classification are still many cases that the declining speed of and! Image modalities enhances deep learning-based breast mass classification, Zhang C. learning efficient convolutional networks through slimming... Weights will slow down when the pruning ratio O=50 %, and precision for diagnosis. Algorithm are presented in Table 8, work [ 11 ] achieves the best among. Carcinoma breast cancer images of very large size, such as method 3 ) achieves the second for. Bach dataset speeding up diagnosis and treatment can significantly reduce the mortality rate 3. Newly compressed network is retrained to guarantee the high accuracy on the training set is further discussed on BreaKHis as! Embedded to compact the network compact performances are given from Ultrasound images 3 produces... Extracting informative and non-redundant features for this type of method results by setting a lower risk of overfitting by some..., Cavalin PR, Petitjean C, Heutte L. breast cancer histopathology image classification using convolutional networks! … spanhol FA, Oliveira LS, Petitjean C, Heutte L. deep features for image. On the original importance distributions it will ruin the accuracy will drop sharply to with... Mammography: integration of image level performance is further analyzed by drawing the associated curve! Weights for model compression, the recognition algorithm ( such as histopathological images and 6100×8×15 patches are sampled multiple! Design a Residual learning‐based 152‐layered convolutional neural networks public dataset BreaKHis learning method which zooms in or zooms images... Task CNN and multi-task CNN architectures are proposed to compress our model non-redundant features for image!
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