MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY OPEN UNIVERSITY Ae MASTER OF SCIENCE IN COMPUTER SCIENCE A computer vision-based method for breast cancer histopathological image classification by deep learning approach Student: Mai Bui Thuy Huynh Thesis Advisor: Dr. Vinh Truong Hoang Ho Chi Minh City, October 2019 Contents Acknowledgment iii Abstract Notations vii Abbreviations ix 1 Literature review of breast cancer histopathological image classification 11 IntroducHon and general consideratons. Q LH HQ HQ HQ Q Q Q Q1 v2 13 Contributionofthethesis. QQ LH va 10 14 Structure ofthethesis.
Q Q LH Q Q Q Q Q QVv2 10 15 Methodology .0 0000002 eee eee 10 2 Foundational theory 13 2. HQ HH HH HH 13 2.11 Introduction to deep neuronnefwortk.2 Present the techniques of neuron network training.3 Present the popular deep network models .2_ Generative Adversarial Networks(GAN). 24 3 Experiment and Discussion 3. Experinental result on three datasets.4 Comparing to handcrafted features and deep features for classification.
List of Tables 41 List of Figures 41 Bibliography 44 Acknowledgment 11 Abstract Computer vision field has became more active in the recent decades when scientists found to apply mathematical and quantitative analysis. Various applications have been using computer vision techniques to improve their productivity such as visual surveillance, robotic, autonomous vehicle, and specially medical image processing. Until Geoffrey Hinton and Yann LeCun, both known as “Godfather of deep learning” used Neural Networks and Back Propagation in characters and handwritten predic- tion given the best result comparing to previous works, the techniques has been be- came prominent. In this thesis, we focus to detect the breast cancer with high accuracy in order to decrease the examination cost in accepted time.
50, we choose the deep learning to re- search and evaluate our approach on three datasets such as BreaKHis, BACH and IDC. Due to some limitations of deep learning and dataset sizes, we propose the composi- tion of popular techniques to be boosting the efficient classification, they are transfer learning, Generative Adversarial Network (GAN) and neural networks. VGG16 & VGG19 are the base models which are applied to extract the high level features space from patch cropped images, naming as multi deep features before being trained by neuron nets. So far, there are not any works to leverage GAN power to generate the fake BreaKHis and in our thesis, we use Pix2Pix and StyleGAN model as gen- erator model.
With the proposed approach, the cancer detection results achieve the better performance to some existing works with 98% in accuracy for BreaKHis, 96% for BACH and 86% for IDC. Notations Number of block of layers are stacked together The hypothesis space in traditional machine learning Loss function for each hypothesis Activation function in deep learning Mapping function in deep learning Input feature Feature’s weight Output feature Loss function in GAN model Discriminator model Generator model Noise input Mean Variance Vii Abbreviations LBP Local Binary Pattern WHO World Health Organization GLOBOCAN Global Cancer Incidence, Mortality and Prevalence CBE Clinical Breast Exam CLBP Completed Local Binary Pattern LPO Local Phase Quantization GLCM Gray Level Co-Occurrence Matrices PFTAS Free Threshold Adjacency Statistic ORB Oriented FAST and Rotated BRIEF k-NN k-Nearest Neighbor SVM Support Vector Machines RF Random Forest ODA Quadratic discriminant analysis GPU Graphic Processing Unit CNN Convolution neuron network CONV Convolutional layer FC Fully connected layer MAE Manifold Persevering Autoencoder DT Decision Tree LR Logistic Regression GAN Generative Adversarial Network MRI Magnetic Resonance Image SIFT Scale Invariant Feature Transform SURF Speeded Up Robust Features SGD Stochastic Gradient Descent 1X Chapter 1 Literature review of breast cancer histopathological image classification 1.1 Introduction and general considerations Cancer is a public health problem in the world today. Among them, breast cancer is the most common invasive cancer in women and have a significant impact to 2.1 million people yearly. In 2018, the World Health Organization (WHO) estimated 627,000 death cases because of breast cancer, be getting 15% death causes.
As a result in 2018 from Global Cancer Incidence, Mortality and Prevalence, GLOBOCAN [1] about a number of new and death cases of 36 cancer types from 185 countries though 5 continents shown in Table 1.1, new breast cases is 11.6% and second leading cause of death cancer. Cancer site No. of new cases | No. of deaths (% of all sites) (% of all sites Lung 2,093,876 (11.8%) Nonmelanoma of skin | 1,042,056 (5.1: Top 10 of popular cancer sites sorted by new cases in descending order Specially in 2012, GLOBOCAN estimated Vietnam — South-Eastern Asia country with low per capital income about 3,200$/year and 20$/year for voluntary medical expense — that this breast cancer was 23/100,000 and had the risen trend [2].
Early cancer detection has many changes to treat and increase survival rate for pa- tients. WHO finds that there are the effective diagnostic methods such as X-ray, Clin- ical Breast Exam (CBE) but this needs to have the professional physicians or experts. Beside the diagnostic result is not always 100% accuracy because of some reasons such as subjective experiments, expertise, emotional state. In recent years, trend of image processing field and machine learning proved that physician can employ this technology to make diagnosis via medical image.
Medical image processing method has been applied much on cancer diagnosis [3] and other diseases [4] with high accuracy in short time. Image diagnosis by machine learning is cost efficient method in Vietnam’s urban region where there is no any professional medical teams. For the most part, researches demonstrated the improvement of breast cancer clas- sification accuracy [5, 6,7, 8,9] but it doesn’t achieve to the significantly high rate. A main reason is a limited on training dataset, as well collecting and annotating suffi- cient data by pathological expert is time-consuming and expensive.
Nowadays there are open-access breast cancer databases for research and litera- ture such as BreaKHis, ICIAR 2018 BACH Challenge, Kaggle breast histopathological image, Tumor Proliferation Assessment Challenge 2016. But almost works experi- mented on BreaKHis [8] built on collaboration with the P&D Laboratory - Pathologi- cal Anatomy and Cytopathology, Parana, Brazil, it means that those results can’t gain the same accuracy on new dataset. Deep learning is a branch of machine learning, representing data characteristic by layers from simple symbols of point, line to complex, abstract structure of polygon. In 1986, Rina Dechter first introduced deep learning in machine learning community.
In 1970, multiplayer perceptrons algorithm stimulated the capacity of human brain to recognize and discriminate objects, especially many applications in computer vision. Then, Yann Lecun achieved the good result of digit handwriting classification by back- propopation in deep learning in 1987. Nowadays, deep learning has been developing so quickly and widely as well applications in many fields. Indeed, although BreaKHis is breast cancer benchmark database, it is not as large as ImageNet which are built on collaboration with Stanford University, Princeton Uni- versity, Google Company and A9 Research.
ImageNet includes 14,197,122 images and 20,000 categories used much on deep learning. New machine learning researches have been getting high accuracy breast cancer 2 classification by many various supervised and unsupervised learning algorithms. For literature reviews from 2016 to May 2019, this thesis studies three main techniques: handcrafted and /or deep feature, transfer learning, generative adversarial network. Handcrafted-feature or deep feature: Spanhol et al.
[10], Badejo et al. To improve the accu- racy to a range of 98.5% - 100%, the Spanhol combined the boosting techniques of 1-NN, QDA, RE, SVM. But the best result is efficient for 40x and 400x magnification. Author concluded that PFTAS feature is suitable for medical image.
Graphic Pro- cessing Unit (GPU) development for big data processing, Spanhol et al. [5] proposed deep learning algorithm which is convolution neuron network (CNN) as CONV-Max Pool-CONV-Average Pool-FC-FC with 32x32 and 64x64 window patch size in se- quence and random order. Accuracy range is 76. Gupta et al.
[12] explored the jointly color-texture information (RGB and HSV color space) with and without stain normalization and various contemporary classifiers used in Spanhol’s work. Song et al. [13], [14] suggested Fisher Vector combined with CNN to be popular in general computer vision. But this descriptor is high dimensionality.
In two years, the authors presented different encoding methods to get it more discrim- inated features such as intra-embedding algorithm and Fisher Vector descriptor 2 x 512 x N extracted from VGG19 and GMM model with N Gaussian components. Qi et al. [15] have pointed to use entropy-based and confidence-boosting strategy as deep active learning method for small training dataset classification which reduces anno- tation costs up to 66.67% with high accuracy from 88. Mukkamala et al.
[16] has built a deep learning technique based on principal component analysis for each channel of LAB color space and SVM with accuracy from 85. Kumar et al. [17] built a CNN model as 3CONV[5x5]-3CONV][3x3]-ReLU-Pool-FC to extract the deep features from medical images. Gupta et al.
[18] found that histopatho- logical stain normalization before using handcrafted-feature extraction will get the cancer classification to be more efficient than gray scale image. Feng et al. [19] ex- ploited the unsupervised learning capacity by using autoencoder network named as manifold persevering autoencoder (MAE) to learn the encoded features from input and then decoding hidden presentations to output. For a new algorithm, Feng et al archived accuracy from 82% to 99.
Reza et al. [20] experimented the sampling techniques such as Under-sampling, Over-sampling, ADASYN, SMOTE with CNN 3 network and found that unbalance data will be effecting to accuracy. Deploying over- sampling method to unbalance BreaKHIs dataset gets the better performance. Angara et al.
[21], Guillén-Rondon et al. Alom et al. [6] combined the strength of Inception, ResNet and Recurrent Convolutional Neural Network with 95% and 97% accurate classifica- tion with/without augmentation for 4 magnification factors. Two core ideas of Zhang et al.
[23] are to use skip connection in Resnet to solve the optimization issues when network becomes deeper and CBAM to refine Resnet features. This method gained the 92.6% highest accuracy for 200x and lowest 88. Sudharshan et al. [5] compared various multiple Instance Learning (MIL) together and concluded that non-parametric MIL,which extracted MIL feature space using a Parzen window technique and k-NN classifier, is higher accuracy than MILCNN and Single Instance Learning, with 40x magnification rate its accuracy is 92.
Roy et al. [24] proposed a patch-based classifier using CNN network consisting of 6CONV-5POOL-3FC. Au- thor experimented this model with 512x512 patch size based data which contained more information and was efficient size, gained 92.% accuracy on ICIAR 2018. Alireza- zadeh et al.
[25] learned features space from two different domains using LBP, LPQ, PFTAS and then forming a projection matrix, in this case that domain space is be- nign and malignant. This method gave the better performance than using each sep- arated LBP, PFTAS, LPQ or CNN feature and classifier as Spanhol’s work. Fondén et al. [26] extracted 3 features type such as the nuclei-based feature by transform- ing to CMYK color space and K-mean clustering algorithm; region-based vector of pink/violet, pink/white, white/violet; texture feature consisting of first order statistic vector, LBP and sparse texture descriptor.
Fond6n used 9 classifier to detect cancer tumor using dataset through Bioimaging 2015 Grand Challenge. Transfer learning technique: Weiss et al. [27] evaluated different feature extrac- tor using VGG, ResNet and Xception in training a limited number of samples and achieved state-of-the-art results on BACH dataset. This method downsized BACH image to 1024x768 in order to train the classification model.
Vo et al. [7] applied the augmentation method as rotate, cut, transform image to increase the training data volumes before extracting deep feature from Inception-ResNet-v2 model in order to avoid the over-fitting. They trained the model with multi-scale input images 600x600, 450x450, 300x300 to extract local and global feature. Then Gradient Boosting Trees model again was trained to detect breast cancer.
Fusion model will vote the higher ac- curacy classifier. The accuracy rate archived to 93.9% at low cost computation. Murtaza et al.