Densely connected convolutional networks for breast cancer histopathological image classification

MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification

Breast cancer is the second leading cause of cancer death among women. Breast cancer is not a single disease, but rather is comprised of many different biological entities with distinct pathological features and clinical implications. Pathologists face a substantial increase in workload and complexity of digital pathology in cancer diagnosis due to the advent of personalized medicine, and diagnostic protocols have to focus equally on efficiency and accuracy. Computerized image processing technology has been shown to improve efficiency, accuracy and consistency in histopathology evaluations, and can provide decision support to ensure diagnostic consistency. We show that convolutional neural networks (CNN) can be an effective tool in classifying breast cancer histopathological images and evaluate its performance as a binary classifier in the field of breast cancer diagnosis using whole slide imaging.

Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.

Breast cancer is the most prevalent cancer globally and presents a significant threat to human health. The low survival rate for advanced stages highlights the importance of early and accurate diagnosis. Pathology images, providing insights into cancer's cellular aggressiveness, are crucial for breast cancer diagnosis. Advances in deep learning have transformed automated medical image diagnosis. However, traditional deep learning methods, reliant on large labeled datasets, face challenges with the typically small size of medical image datasets. To overcome this, our study combines deep transfer learning (DTL) with convolutional neural networks(CNN). We utilize and enhance a ResNet50 model pretrained on a large dataset. Additionally, we apply four different deep transfer learning methods to feature extraction and classification of the BreakHis dataset, aiming to determine if breast cancer is benign or malignant. Ultimately, our system achieved the best average accuracy rates under four different magnifications: 98.57%, 96.83%, 97.76%, and 97.16%. These results demonstrate the accuracy and potential of deep transfer learning in classifying breast cancer histopathological images and its promising application in tumor prediction.

Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification

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.

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.

Breast cancer is a highly heterogeneous malignant tumor, and its accurate classification is of great significance for clinical diagnosis and treatment decision-making. In recent years, convolutional neural networks and Transformer have been widely used in pathological image analysis of breast cancer. Though the former excels at capturing local information and the latter is adept at modeling global dependencies, the former is limited by fixed sampling positions and is hindered to characterize irregular cell morphology, the latter is insufficient in describing the two-dimensional spatial structure of cells and tissues. Based on these, a Spatial-Frequency Domain Feature Extraction Model (S-FDFEM) proposed in this paper integrates spatial and frequency domain information to enhance feature learning for pathological image recognition. Specifically, in the spatial domain, Deformable Bottleneck Convolution (DBottConv) is utilized to effectively represent intricate cell and tissue morphological variations in pathological images and improve the expression ability of local features; in the frequency domain, wavelet low frequencies and Fourier high frequencies are generated based on the input pathological images to capture global approximation and fine structures, then statistical transformer and depth gradient feature extraction modules are integrated to operate on these two frequency domain components, enabling global dynamic focusing and two dimensions spatial characterization of pathological images. Experimental results show that the classification of breast cancer pathological image on BreakHis and BACH datasets verify the superiority of the S-FDFEM proposed in this paper.

Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model’s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.

Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last decade of achieving state-of-the-art performance in classification and localization tasks. The convolutional neural network, a deep learning framework, provides remarkable results in tissue images analysis, but lacks in providing interpretation and reasoning behind the decisions. We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images. We frame the image classification problem as weakly supervised multiple instance learning problem where an image is collection of patches i.e. instances. Attention-based multiple instance learning (A-MIL) learns attention on the patches from the image to localize the malignant and normal regions in an image and use them to classify the image. We present classification and localization results on two publicly available BreakHIS and BACH dataset. The classification and visualization results are compared with other recent techniques. The proposed method achieves better localization results without compromising classification accuracy.

This chapter looks at several commonly used breast cancer datasets, whether they are text or images, as well as related works demonstrating previous efforts for breast cancer diagnosis. Wisconsin breast cancer dataset downloaded from the UCI machine learning repository in this part. This dataset is utilized to separate between cancerous and benign tumors. The breast carcinoma subtyping dataset is a vast collection of annotated Hematoxylin and Eosin-stained pictures designed to aid in the diagnosis of breast lesions. The breast cancer semantic segmentation dataset comprises approximately 20,000 tissue region segmentation annotations from the cancer genome Atlas breast cancer pictures. The breast cancer histopathological image classification is made up of 9109 microscopic images of breast tumor tissue collected from 82 people at various magnification levels. PatchCamelyon is a dataset for picture categorization. It is made up of 327.680 color pictures taken from lymph node histopathologic scans.

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.

Breast cancer remains one of the most common malignancies in women, where early diagnosis is critical for effective treatment. While deep learning shows significant promise for histopathological image analysis, its performance is often hindered by two key challenges: the scarcity and imbalance of medical datasets, and the inherent difficulty of classifying images with large intraclass and small interclass variations. To address these issues, we propose a novel Center‐Border Dual‐Branch Network with Dynamic Weighted Fusion (CBDF‐Net) for breast cancer histopathology image classification. Our framework first counteracts data imbalance through a pathology‐informed augmentation pipeline (HistoAugment), then employs a Center‐Border Partition Module (CBPM) for spatially differentiated feature extraction. These distinct representations are subsequently processed by a dynamically fused dual‐branch network (DFD‐Net), and its outputs are then integrated by a dynamic fusion mechanism with a compensation shortcut. The entire architecture is trained under the supervision of our novel Adaptive Entropy Penalty (AEP) Loss, which mitigates overconfidence and enhances generalization. Extensive experiments on multiple public datasets demonstrate that CBDF‐Net consistently and significantly outperforms state‐of‐the‐art methods.

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

Classify image into benign and malignant is one of the basic image processing tools in digital pathology for breast cancer diagnosis. Deep learning methods have received more attention recently by training with large-scale labeled datas, but collecting and annotating clinical data is professional and time-consuming. The proposed work develops a deep active learning framework to reduce the annotation burden, where the method actively selects the valuable unlabeled samples to be annotated instead of random selecting. Besides, compared with standard query strategy in previous active learning methods, the proposed query strategy takes advantage of manual labeling and auto-labeling to emphasize the confidence boosting effect. We validate the proposed work on a public histopathological image dataset. The experimental results demonstrate that the proposed method is able to reduce up to 52% labeled data compared with random selection. It also outperforms deep active learning method with standard query strategy in the same tasks.