Breast Ultrasound Classification Research Articles

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach

BackgroundBreast cancer is the most common cancer among women, and ultrasound is a usual tool for early screening. Nowadays, deep learning technique is applied as an auxiliary tool to provide the predictive results for doctors to decide whether to make further examinations or treatments. This study aimed to develop a hybrid learning approach for breast ultrasound classification by extracting more potential features from local and multi-center ultrasound data.MethodsWe proposed a hybrid learning approach to classify the breast tumors into benign and malignant. Three multi-center datasets (BUSI, BUS, OASBUD) were used to pretrain a model by federated learning, then every dataset was fine-tuned at local. The proposed model consisted of a convolutional neural network (CNN) and a graph neural network (GNN), aiming to extract features from images at a spatial level and from graphs at a geometric level. The input images are small-sized and free from pixel-level labels, and the input graphs are generated automatically in an unsupervised manner, which saves the costs of labor and memory space.ResultsThe classification AUCROC of our proposed method is 0.911, 0.871 and 0.767 for BUSI, BUS and OASBUD. The balanced accuracy is 87.6%, 85.2% and 61.4% respectively. The results show that our method outperforms conventional methods.ConclusionsOur hybrid approach can learn the inter-feature among multi-center data and the intra-feature of local data. It shows potential in aiding doctors for breast tumor classification in ultrasound at an early stage.

A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

Distilling Knowledge From an Ensemble of Vision Transformers for Improved Classification of Breast Ultrasound

To develop a deep learning model for the automated classification of breast ultrasound images as benign or malignant. More specifically, the application of vision transformers, ensemble learning, and knowledge distillation is explored for breast ultrasound classification. Single view, B-mode ultrasound images were curated from the publicly available Breast Ultrasound Image (BUSI) dataset, which has categorical ground truth labels (benign vs malignant) assigned by radiologists and malignant cases confirmed by biopsy. The performance of vision transformers (ViT) is compared to convolutional neural networks (CNN), followed by a comparison between supervised, self-supervised, and randomly initialized ViT. Subsequently, the ensemble of 10 independently trained ViT, where the ensemble model is the unweighted average of the output of each individual model is compared to the performance of each ViT alone. Finally, we train a single ViT to emulate the ensembled ViT using knowledge distillation. On this dataset that was trained using five-fold cross validation, ViT outperforms CNN, while self-supervised ViT outperform supervised and randomly initialized ViT. The ensemble model achieves an area under the receiver operating characteristics curve (AuROC) and area under the precision recall curve (AuPRC) of 0.977 and 0.965 on the test set, outperforming the average AuROC and AuPRC of the independently trained ViTs (0.958±0.05 and 0.931±0.016). The distilled ViT achieves an AuROC and AuPRC of 0.972 and 0.960. Both transfer learning and ensemble learning can each offer increased performance independently and can be sequentially combined to collectively improve the performance of the final model. Furthermore, a single vision transformer can be trained to match the performance of an ensemble of a set of vision transformers using knowledge distillation.

Classification of tumor in one single ultrasound image via a novel multi-view learning strategy

Computer-aided diagnosis (CAD) technology has been widely used in the early diagnosis of breast cancer. Nowadays, most of the existing breast ultrasound classification methods need to crop a tumor-centered image (TCI) on each image as the input of the system. These methods ignore the fact that the tumor as well as its surrounding tissues can actually be viewed from multiple aspects, and it is difficult to extract multi-resolution information applying only a single view image. In addition, the current methods do not effectively extract fine-grained features, and subtle details play an important role in breast classification. In our research, we propose a novel strategy to generate multi-resolution TCIs in a single ultrasound image, resulting in a multi-data-input learning task. Hence, a conventional single image based learning task is converted into a multi-view learning task, and an improved combined style fusion method suitable for a deep network is proposed, which integrates the advantage of the decision-based and feature-based methods to fuse the information of different views. At the same time, we first attempt to introduce the fine-grained classification method into breast classifications and capture the pairwise correlation between feature channels at each position to extract subtle information. The comparative experimental results show that our method can effectively improve the classification performance and achieves the best results in five metrics.

Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagno-sis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women’s Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.

A Breast Ultrasound Classification Method Based on High-Level Semantic Features Mapping

A Breast Ultrasound Classification Method Based on High-Level Semantic Features Mapping

Computer-aided diagnosis system for breast cancer using B-mode and color Doppler flow images

To improve the diagnostic accuracy of breast ultrasound classification, a novel computer-aided diagnosis system based on B-Mode ultrasound and color Doppler flow imaging is proposed. First, different feature extraction methods were used to obtain the texture and geometric features from B-Mode ultrasound images. In the color Doppler feature stage, both vascularity and hemodynamic features are studied by applying color distribution and periodograms analysis to Doppler signals. Finally, a support vector machine classifier with selected feature vectors is used to classify breast tumors into benign and malignant. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and areas under the ROC curves of the proposed system are 94.28%, 96.36%, 92.00%, 92.98%, 95.83%, and 0.9679, respectively. The experimental results demonstrate that the proposed computer-aided diagnosis system can improve the true-positive and decrease the false-positive diagnostic rate, which is useful for reducing the unnecessary biopsy and death rate.