Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability. To address this issue, this paper proposes a novel end-to-end framework for breast cancer diagnosis: lesion asymmetry screening assisted global awareness multi-view network (LAS-GAM). More than just the most common image-level diagnostic model, LAS-GAM operates at the patient level, simulating the workflow of radiologists analyzing mammographic images. The framework processes the four views of a patient and revolves around two key modules: a global module and a lesion screening module. The global module simulates the comprehensive assessment by radiologists, integrating complementary information from the craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts to generate global features that represent the patient's overall condition. The lesion screening module mimics the process of locating lesions by comparing symmetric regions in contralateral views, identifying potential lesion areas and extracting lesion-specific features using a lightweight model. By combining the global features and lesion-specific features, LAS-GAM simulates the diagnostic process, making patient-level predictions. Moreover, it is trained using only patient-level labels, significantly reducing data annotation costs. Experiments on the Digital Database for Screening Mammography (DDSM) and In-house datasets validate LAS-GAM, achieving AUCs of 0.817 and 0.894, respectively.

Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer-aided diagnosis (CAD) systems. With the development of deep learning in CAD, deep convolutional neural networks have been shown to have the ability to complete the classification of breast cancer tumor patches with high quality, which makes most previous CNN-based full-field mammography classification methods rely on region of interest (ROI) or segmentation annotation to enable the model to locate and focus on small tumor regions. However, the dependence on ROI greatly limits the development of CAD, because obtaining a large number of reliable ROI annotations is expensive and difficult. Some full-field mammography image classification algorithms use multi-stage training or multi-feature extractors to get rid of the dependence on ROI, which increases the computational amount of the model and feature redundancy. In order to reduce the cost of model training and make full use of the feature extraction capability of CNN, we propose a deep multi-scale region selection network (MRSN) in deep features for end-to-end training to classify full-field mammography without ROI or segmentation annotation. Inspired by the idea of multi-example learning and the patch classifier, MRSN filters the feature information and saves only the feature information of the tumor region to make the performance of the full-field image classifier closer to the patch classifier. MRSN first scores different regions under different dimensions to obtain the location information of tumor regions. Then, a few high-scoring regions are selected by location information as feature representations of the entire image, allowing the model to focus on the tumor region. Experiments on two public datasets and one private dataset prove that the proposed MRSN achieves the most advanced performance.

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in making more objective and accurate judgments. However, existing methods often depend on datasets with manual segmentation annotations. In addition, due to the large image sizes and small lesion proportions, many methods that do not use region of interest (ROI) mostly rely on multi-scale and multi-feature fusion models. These shortcomings increase the labor, money, and computational overhead of applying the model. Therefore, a deep location soft-embedding-based network with regional scoring (DLSEN-RS) is proposed. DLSEN-RS is an end-to-end mammography image classification method containing only one feature extractor and relies on positional embedding (PE) and aggregation pooling (AP) modules to locate lesion areas without bounding boxes, transfer learning, or multi-stage training. In particular, the introduced PE and AP modules exhibit versatility across various CNN models and improve the model's tumor localization and diagnostic accuracy for mammography images. Experiments are conducted on published INbreast and CBIS-DDSM datasets, and compared to previous state-of-the-art mammographic image classification methods, DLSEN-RS performed satisfactorily.

Early diagnosis of breast cancer is challenging because in the most common mammogram images, the tumor usually occupies only a very small part of the entire image, which often makes deep learning models lose attention to the tumor area. In previous work, most models solved this problem by using ROI labeling to train models, which was expensive and difficult to widely apply. Some recent ROI-free methods use multi-scale features or multi-stage training, which gets rid of the model's dependence on ROI but greatly increases the computational complexity and deployment difficulty, limiting the potential of deep neural networks. Therefore, a deep positional-representation-based local information retention networks (PR-LIR) was proposed. PR-LIR is a lightweight, end-to-end mammogram classification model, which uses positional representation (PR) and multi-scale regional pooling (MRP) modules to locate tumor regions and retain regional semantic information of small target tumors at different scales, without ROI labeling and multi-stage training, and almost no increasement in parameters and computational complexity. In particular, the proposed PR and MRP modules have good generalization performance, which can be applied to most CNN models and improve the classification accuracy of mammography images. Experimental results on two publicly available datasets show that PR-LIR achieves the best AUC and satisfactory accuracy compared to the previous state-of-the-art mammogram classification method.