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Mammography is an important screening criterion for breast cancer, one of the major diseases causing numerous deaths among female patients. Meanwhile, manual diagnosis of mammography is a time-consuming and labor-consuming job. Mammogram classification based on deep learning plays a vital role in computer-aided diagnosis (CAD) systems to mitigate the pressure on physicians. This paper proposes a learning-based multi-view mammogram classification model that captures long-distance dependence and extracts features of multiple receptive fields. Our model considers global and local features of mammography images using Transformer for global features and the proposed multiplex convolutions module for local features. We evaluate our proposed method on a dataset of mammography images obtained from a hospital in China. The proposed method achieves 90.57% accuracy and 94.86% AUC in benign or malignant classification tasks and outperforms other advanced methods for mammogram classification. It is worth noting that the proposed method only requires image-level labels and acts on the whole raw mammogram, which has clinical significance. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most serious disease affecting women's health. Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer. However, ultrasound images are low resolution and poor quality. Thus, developing accurate detection system is a challenging task. In this paper, we propose a fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints. In the first part, the image is pre-processed by contrast enhancement, and wavelet features are employed for image augmentation. A fuzzy membership function transforms the augmented BUS images into the fuzzy domain. The features from convolutional layers are processed using fuzzy logic as well. The conditional random fields (CRFs) post-process the segmentation result. The location relation among the breast anatomy layers is utilized to improve the performance. The proposed method is applied to the dataset with 325 BUS images, and achieves state-of-the-art performance compared with that of existing methods with true positive rate 90.33%, false positive rate 9.00%, and intersection over union (IoU) 81.29% on tumor category, and overall intersection over union (mIoU) 80.47% over five categories: fat layer, mammary layer, muscle layer, background, and tumor. [ABSTRACT FROM AUTHOR]

• Ensemble of MultiScale Networks (EMS-Net) for breast histology image classification. • Extract visual features using multiple deep convolutional networks at multiscale. • Select the optimal subset of fine-tuned deep models and combine them to EMS-Net. • Achieved an accuracy of 90.00% on the BACH Challenge classification task (Part A). Histology images analysis resulted from needle biopsy serves as the gold standard for breast cancer diagnosis. Deep learning-based classification of breast tissues in histology images, however, is less accurate, due to the lack of adequate training data and ignoring the multiscale structural and textural information. In this paper, we propose the Ensemble of MultiScale convolutional neural Networks (EMS-Net) to classify hematoxylin–eosin stained breast histopathological microscopy images into four categories, including normal tissue, benign lesion, in situ carcinoma, invasive carcinoma. We first convert each image to multiple scales, and then use the training patches cropped and augmented at each scale to fine-tune the pre-trained DenseNet-161, ResNet-152, and ResNet-101, respectively. We find that a combination of three fine-tuned models is more accurate than other combinations, and use them to form an ensemble model. We evaluated our algorithm against three recent methods on the BACH challenge dataset. It shows that the proposed EMS-Net algorithm achieved an accuracy of 91.75 ± 2.32% in the five-fold cross validation using 400 training images, which is higher than the accuracy of other three algorithms, and also achieved an accuracy of 90.00% in the online verification using 100 testing images. [ABSTRACT FROM AUTHOR]