Breast Cancer Histopathological Image Classification Research Articles

Semi‐supervised breast histopathological image classification with self‐training based on non‐linear distance metric

Histopathological analysis requires a lot of clinical experience and time for pathologists. Artificial intelligence (AI) may have an important role in assisting pathologists and leading to more efficient and effective histopathological diagnoses. To address the challenge of requiring a large number of labelled images to train deep learning models in breast cancer histopathological image classification, a self-training semi-supervised learning method consisting three components is proposed: Firstly, a pre-trained ResNet-18 was used to extract features and generate pseudo-labels for unlabelled data; secondly, a relational weight network based on the squeeze-and-excitation network (SENet) was trained to calculate the non-linear distance metrices between labelled and unlabelled samples, in order to improve the accuracy of pseudo-labelling; lastly, a consistency loss—maximum mean difference (MMD)—was added into the model to minimize the divergence between distributions of unlabelled and labelled samples. Extensive experiments were conducted on the open access BreakHis dataset. The proposed method outperformed the state-of-the-art semi-supervised methods at all tested annotated percentages (10–70%), and also achieved comparable performance with supervised methods at higher annotated percentages (50%, 70%).

The Xception model: A potential feature extractor in breast cancer histology images classification

Computer-assisted pathology analysis is an emerging field in health informatics and extremely important for effective treatment. Herein, we demonstrated the ability of the pre-trained Xception model for magnification-dependent breast cancer histopathological image classification in contrast to handcrafted approaches. The Xception model and SVM classifier with the ‘radial basis function’ kernel has achieved the best and consistent performance with the accuracy of 96.25%, 96.25%, 95.74%, and 94.11% for 40X, 100X, 200X and 400X level of magnification, respectively. A comparison with existing state-of-the-art techniques has been conducted based on accuracy, recall, precision, F1 score, area under ROC and precision–recall curve evaluation metrics.

Second-order asymmetric convolution network for breast cancer histopathology image classification.

Recently, convolutional neural networks (CNNs) have been widely utilized for breast cancer histopathology image classification. Besides, research works have also convinced that deep high-order statistic models obviously outperform corresponding first-order counterparts in vision tasks. Inspired by this, we attempt to explore global deep high-order statistics to distinguish breast cancer histopathology images. To further boost the classification performance, we also integrate asymmetric convolution into the second-order network and propose a novel second-order asymmetric convolution network (SoACNet). SoACNet adopts a series of asymmetric convolution blocks to replace each stand square-kernel convolutional layer of the backbone architecture, followed by a global covariance pooling to compute second-order statistics of deep features, leading to a more robust representation of histopathology images. Extensive experiments on the public BreakHis dataset demonstrate the effectiveness of SoACNet for breast cancer histopathology image classification, which achieves competitive performance with the state-of-the-arts.

MobiHisNet: A Lightweight CNN in Mobile Edge Computing for Histopathological Image Classification

Recent advances in artificial intelligence (AI), especially convolutional neural networks (CNNs), alongside the digitization of histopathological images, have made the computer-aided diagnosis of breast cancer a reality. However, deep learning-based approaches are computationally expensive and have huge parameters, which makes them less affordable for edge devices. In order to make them affordable for edge devices, the whole classification model needs to be compressed while maintaining accuracy. Providing a low-cost solution for histopathological diagnosis in the recent edge-computing world is of utmost importance. Therefore, in this study, we propose “MobiHisNet,” an efficient and lightweight CNN model for histopathological image classification (HIC) based on MobileNet. MobiHisNet was successfully deployed on a Raspberry Pi, as well as three mobile devices, demonstrating its ability to run on a lightweight and portable processor. Our studies indicated that a depth parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma = 0.5$ </tex-math></inline-formula> ) and a 16-bit quantization are the optimum parameters for the proposed model while balancing the accuracy, inference time, and memory peak requirements. Compared to the state-of-the-art, pretrained models, MobiHisNet has fewer parameters and calculations, resulting in faster image classification. This renders it more viable for production purposes and applications on edge devices. In addition, MobiHisNet is computationally faster than VGG16, ResNet50, Xception, and InceptionV3 by twenty-seven, eight, six, and five times, respectively. This also outperforms all the baseline models with the moderate model size and FLOP counts. Experiments on breast cancer HIC (BreakHis) data sets show superior performance of MobiHisNet on edge devices in terms of higher accuracy, lesser complexity, and lesser memory requirements. Thus, it has a high potential for deployment in mobile edge devices.

Predicting and Classifying Breast Cancer Using Machine Learning

The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.

A Review of Breast Cancer Histopathological Image Classification

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis.

Data limitation is one of the major challenges in applying deep learning to medical images. Data augmentation is a critical step to train robust and accurate deep learning models for medical images. In this research, we increase the size of a small dataset by using an Auxiliary Classifier Generative Adversarial Network (ACGAN) which generates realistic images along with their class labels.We evaluate the effectiveness of our ACGAN augmentation method by performing breast cancer histopathological image classification with deep convolutional neural network (dCNN) classifiers trained on our enhanced dataset. For our classifier, we use a transfer learning approach where the convolutional features are extracted from a pertained model and subsequently fed into several extreme gradient boosting (XGBoost) classifiers. Our experimental results on Breast Cancer Histopathological (BreakHis) dataset show that ACGAN data augmentation, along with our XGBoost classifier increases the classification accuracy by 9.35% for binary classification (benign vs. malignant) and 8.88% for four-class tumor sub-type classification compared with standard transfer learning approach.

Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.

Evolution of histopathological breast cancer images classification using stochastic dilated residual ghost model

Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting concerns caused by a lack of histopathology images. 3) Using the proposed stochastic dilated convolution (SDC) model, able to find missing features and improve tiny and low-level features such as edge, contour, and color correctness. This model effectively solves depth issues with stochastic pooling and max pooling by combining three proposed units: spatial dilation convolution, channel dilation convolution, and dilated convolution to detect missing features and obtain more information on images. Stochastic pooling may choose any value since it is classified as a heterogeneous approach that can determine tiny and large values. When the max pooling and stochastic pooling are combined, a decent feature map is produced, processed by the dilatation unit, detecting cancer cells without adding to the complexity. 4) To correctly identify breast cancer and extract depth characteristics, the stochastic dilated residual ghost (SDRG) model uses the proposed SDC model, a ghost unit, stochastic upsampling, and downsampling units. The proposed feature selection unit employs linear transformations to produce feature maps to show information based on intrinsic characteristics to eliminate redundant or similar features in convolutional neural networks. Convolution and identity mapping are used to construct and retain intrinsic feature mappings in this skip unit while upsampling with stochastic pooling is used to minimize feature dimensions. The proposed model?s performance was assessed with 150,271 augmented and original histopathology images. The proposed method?s findings compare favorably to eight popular approaches. In addition, the suggested model outperforms the competition in terms of accuracy, average precision score, precision, sensitivity, and f1 score. With an accuracy of 98.41% with BreakHis and 98.60% with the BreCaHAD dataset with enhanced images, the proposed method exceeds numerous state-of-the-art methods.

基于原型的乳腺癌病理图像分类算法

基于原型的乳腺癌病理图像分类算法

Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing

Automatic classification of breast cancer histopathological images is of great application value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights semantics, while capsule network (CapsNet) focuses on detailed information about the position and posture. Combining these information can obtain more discriminative features which is useful to improve the classification performance. In the paper, breast cancer histopathological image classification based on deep feature fusion and enhanced routing (FE-BkCapsNet) is proposed to take advantages of CNN and CapsNet. First, a novel structure with dual channels which can extract convolution features and capsule features simultaneously, integrate sematic features and spatial features into new capsules to obtain more discriminative information is designed. Then, routing coefficients are optimized indirectly and adaptively by modifying the loss function and embedding the routing process into entire optimization process. The proposed method FE-BkCapsNet was tested on a public dataset BreaKHis. Experimental results (40×: 92.71%, 100×: 94.52%, 200×: 94.03%, 400×: 93.54) demonstrate that the proposed method is efficient for breast cancer classification in clinical settings.

Aggregated Deep Global Feature Representation for Breast Cancer Histopathology Image Classification

Convolutional neural networks (CNNs), successfully used in a great number of medical image analysis applications, have also achieved the state-of-the-art performance in breast cancer histopathology image (BCHI) classification problem recently. However, due to the large varieties among within-class images and insufficient data volume, it is still a challenge to obtain more competitive results by using deep CNN models alone. In this paper, we aim to explore the combination of CNN models with a milestone feature representation method in visual tasks, i.e., vector of locally aggregated descriptors (VLAD), for the BCHI classification, and further propose a novel aggregated deep global feature representation (ADGFR) for this problem. ADGFR adopts the deep features that are extracted from the fully connected layer to form an individual descriptor vector, and augments input images to generate different descriptors for achieving the final aggregated descriptor vector. The individual descriptor vector can effectively keep the global features of benign and malignant image, whose discriminability is further reinforced by the aggregate operation, leading to the more discriminant capability of ADGFR for BCHI. Extensive experiments on the public Break His dataset illuminate that our ADGFR can achieve the optimal classification accuracies of 95.05% at image level and 95.50% at patient level, respectively.

An ensemble algorithm for breast cancer histopathology image classification

Breast cancer is considered among prime reasons of deaths among women. Early detection can improve the survival rate and can decrease the mortality rate too. Though it’s a difficult and tedious task for pathologists. The motive of this research is to assess the efficiency and accuracy of various deep learning models and ensemble methods. Authors have proposed Stacked Generalized Ensemble algorithm that classifies the images into benign and malignant. The dataset used in experimentation is H&E breast cancer image dataset. Experimental results show that SGE has outperformed on various deep learning single classifiers. The evaluation criteria used for measuring the efficiency of algorithm is accuracy, precision, recall and F1 measure. A comparative analysis has been done with the existing deep learning methods. 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%.

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification.

Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.

Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models.

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of for carcinoma class and overall accuracy of . Also, it offered an F1 score of . These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

Breast cancer histopathology image classification using kernelized weighted extreme learning machine

AbstractHistopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset.

Breast cancer histopathological image classification using multi-scale channel squeeze-and-excitation model

Breast cancer histopathological image classification using multi-scale channel squeeze-and-excitation model

Convolutional Neural Network Based Breast Cancer Histopathology Image Classification.

Breast cancer is a global health concern, with approximately 30 million new cases projected to be reported by 2030. While efforts are being channeled into curative measures, preventive and diagnostic measures also need to be improved to curb the situation. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have been widely adopted for the computerized classification of breast cancer histopathology images. In this work, we propose a set of training techniques to improve the performance of CNN-based classifiers for breast cancer identification. We combined transfer learning techniques with data augmentation and whole image training to improve the performance of the CNN classifier. Instead of conventional image patch extraction for training and testing, we employed a high-resolution whole-image training and testing on a modified network that was pre-trained on the Imagenet dataset. Despite the computational complexity, our proposed classifier achieved significant improvement over the previously reported studies on the open-source BreakHis dataset, with an average image level accuracy of about 91% and patient scores as high as 95%.Clinical Relevance- this work improves on the performance of CNN for breast cancer histopathology image classification. An improved Breast cancer image classification can be used for the preliminary examination of tissue slides in breast cancer diagnosis.

Residual learning based CNN for breast cancer histopathological image classification

AbstractBiopsy is one of the most commonly used modality to identify breast cancer in women, where tissue is removed and studied by the pathologist under the microscope to look for abnormalities in tissue. This technique can be time‐consuming, error‐prone, and provides variable results depending on the expertise level of the pathologist. An automated and efficient approach not only aids in the diagnosis of breast cancer but also reduces human effort. In this paper, we develop an automated approach for the diagnosis of breast cancer tumors using histopathological images. In the proposed approach, we design a residual learning‐based 152‐layered convolutional neural network, named as ResHist for breast cancer histopathological image classification. ResHist model learns rich and discriminative features from the histopathological images and classifies histopathological images into benign and malignant classes. In addition, to enhance the performance of the developed model, we design a data augmentation technique, which is based on stain normalization, image patches generation, and affine transformation. The performance of the proposed approach is evaluated on publicly available BreaKHis dataset. The proposed ResHist model achieves an accuracy of 84.34% and an F1‐score of 90.49% for the classification of histopathological images. Also, this approach achieves an accuracy of 92.52% and F1‐score of 93.45% when data augmentation is employed. The proposed approach outperforms the existing methodologies in the classification of benign and malignant histopathological images. Furthermore, our experimental results demonstrate the superiority of our approach over the pre‐trained networks, namely AlexNet, VGG16, VGG19, GoogleNet, Inception‐v3, ResNet50, and ResNet152 for the classification of histopathological images.

基于改进ResNeXt的乳腺癌组织病理学图像分类

基于改进ResNeXt的乳腺癌组织病理学图像分类