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.

The multi-temporal remote sensing cloud removal method has improved performance, but it lacks a screening mechanism during feature fusion, simply summing and fusing features from different temporal states. This results in the inclusion of unwanted clouds and redundant feature information, hindering the restoration of the landscape under the clouds. To address this, we propose a selective information fusion generative adversarial network (SIF-GAN) for remote sensing image cloud removal. SIF-GAN incorporates channel attention during feature extraction to capture important information in different channels and uses the selective information fusion network to assign weights to the feature information from other temporal states, selecting the crucial features for fusion. The feature of cloud-free regions in different temporal states is utilized maximally by the selection process to recover the image features under clouds. The results of the experiments show that SIF-GAN achieves superior cloud removal performance compared to other methods.

Optical remote sensing images are widely used in many fields, and local complex texture details in images usually play a critical role in downstream tasks. However, noise interference will destroy the complex texture in the image, thus reducing the accuracy of downstream tasks. The current attention mechanism usually focuses on the global high-level features in the image, so it cannot effectively focus on the high-frequency information in the local complex texture in the remote sensing image, and obtaining clean remote sensing images to train neural networks is difficult. Therefore, applying the current depth learning based natural image denoising methods directly to optical remote sensing images is challenging. To solve these problems, we propose a dynamic adaptive attention guided self-supervised single remote sensing image denoising network (DAA-SSID). We construct a dynamic adaptive attention module (DAAM) by dynamically calculating the activation intensity of each neuron and combining the spatial feature information extracted from remote sensing images. It can effectively extract complex texture features from remote sensing images when only a single remote sensing image participates in training. And we use independent random Bernoulli sampling in the training and inference stages respectively to prevent over-fitting caused by single-image training. Therefore, compared with other self-supervised denoising methods, our proposed model can denoise remote sensing images with more complex textures when only a single image destroyed by noise is used as the training input. Experiments on synthetic additive gaussian noise data and authentic noise data have shown that the proposed model achieves satisfactory results.

In recent years, deep learning-based remote-sensing image super-resolution (SR) methods have made significant progress, and these methods require a large number of synthetic data for training. To obtain sufficient training data, researchers often generate synthetic data via fixed bicubic downsampling methods. However, the synthesized data cannot reflect the complex degradation process of real remote-sensing images. Thus, performance will dramatically reduce when these methods work in real low-resolution (LR) remote-sensing images. This letter proposes a U-shaped attention connection network (US-ACN) for remote-sensing image SR to solve this issue. Our US-ACN does not rely on any synthetic external dataset for training and merely requires one LR image to complete the training. The US-ACN utilizes remote-sensing images’ strong internal feature repetitiveness and fully learns this internal repetitive feature through a well-designed US-ACN to achieve the remote-sensing image SR. In addition, we design a 3-D attention module to generate effective 3-D weights by modeling channel and spatial attention weights, which is more helpful for the learning of internal features. Through the U-shaped connection among attention modules, context information propagation and attention weights learning are fully utilized. Many experiments show that our US-ACN adequately adapts to the remote-sensing image SR in various situations and performs advanced performance.