Accurately extracting buildings from high-resolution remote sensing images is crucial for urban planning, smart city construction, map updating, and other fields. However, the buildings extracted from remote sensing images still have deficiencies such as fuzzy boundaries and poor integrity due to the occlusion of trees and shadows, the interference of redundant information, and the characteristics of buildings with different shapes and scales. In this paper, a novel building extraction model EGAFNet is proposed for remote sensing images, which utilizes EfficientNetV2 as the encoder and multiscale feature enhancement module as the decoder to improve the accuracy and efficiency. To address the issue of missing boundary information, a branch for extract boundary features and an edge guidance module are constructed to enhance the network’s ability to express the boundary. In addition, a scale-aware adaptive fusion module is designed to adaptively aggregate the multi-scale features to enhance the network’s ability to capture the different scale features. We also adopt a multi-layer supervision strategy that predict the outputs for each layer of the decoding phase and impose multi-level loss constraints to achieve fast fitting of the model. Experiments are conducted on the WHU building dataset and a dataset of building instances of typical cities in China. The experimental results demonstrate that the EGAFNet achieves the best F1-score and IoU compared with other typical building extraction methods. It suggests that EGAFNet can effectively improve the learning ability of the boundary information and enhance the multi-scale building feature representation, thus realizing more accurate building extraction. The code will be available at https://github.com/Mwyang/EGAFNet.

BACKGROUND: Virtual touch imaging quantification (VTIQ), a form of shear wave elastography, may help in the diagnosis of breast lesions. OBJECTIVE: We aimed to evaluate the diagnostic performance of combined VTIQ and conventional ultrasound (US), and

Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies in connectivity and completeness due to obstruction by surrounding ground objects, the influence of similar targets, and the slender structure of roads themselves. To address this issue, we propose a novel dual-path convolutional neural network with a strip dilated attention module, named DPSDA-Net, which adopts a U-shaped encoder–decoder structure, combining the powerful advantages of attention mechanism, dilated convolution, and strip convolution. The encoder utilizes ResNet50 as its basic architecture. A strip position attention mechanism is added between each residual block to strengthen the coherent semantic information of a road. A long-distance shortcut connection operation is introduced to preserve the spatial information characteristics of the original image during the downsampling process. At the same time, a pyramid dilated module with a strip convolution and attention mechanism is constructed between the encoder and decoder to enhance the network feature extraction ability and multi-scale extraction of road feature information, expand the model’s receptive field, and pay more attention to the global spatial semantic and connectivity information. To verify the reliability of the proposed model, road extraction was carried out on the Massachusetts dataset and the LRSNY dataset. The experimental results show that, compared with other typical road extraction methods, the proposed model achieved a higher F1 score and IOU. The DPSDA-Net model can comprehensively characterize the structural features of roads, extract roads more accurately, retain road details, and improve the connectivity and integrity of road extraction in remote sensing images.

BACKGROUND: Acoustic radiation force impulse imaging (ARFI) with Virtual Touch Tissue Quantification (VTQ) or Virtual Touch Tissue Imaging Quantification (VTIQ) measures shear wave velocity (SWV), which is proportional to tissue stiffness, a diagnost

Food safety significantly impacts the human health, establishing an effective prediction method to assess food safety risk is crucial for food safety control. At present, in the situation of insufficient detection data, it is necessary to employ data augmentation methods to generate large-scale detection data and accurately capture the long-term variation patterns through safety risk prediction models. However, existing data augmentation methods and safety prediction models face challenges such as gradient vanishing and difficulty in capturing long-term dependencies. Therefore, this paper proposes a Small sample Data Augmentation Multi-step Prediction Method (SDAMPM) to assess wheat flour safety risks. Firstly, we improved time-series generative adversarial networks based on external temporal convolution and Wasserstein distance to expand wheat flour hazard factor detection data. Secondly, we employed expanded data to establish dietary exposure evaluation system for wheat flour, serving as the desired output for multi-step prediction models. Finally, we constructed a stable Informer (Stainformer) multi-step prediction model by designing symmetric ProbSparse self-attention and distilling layer based on dilated causal convolution. Experiments on the wheat flour dietary exposure evaluation system demonstrate that compared to other methods, the expanded data is similar to the distribution of the original data. This approach effectively predicts long-term safety risks associated with wheat flour consumption and can provide assistance and technical support in decision-making for relevant departments, thereby reducing the occurrence of food safety incidents.

With the continuous advancement of urbanization, the importance of building change detection in urban planning, disaster management, and other fields has become increasingly prominent. However, existing change detection methods still have significant limitations in spatial domain difference feature representation and multi-scale difference capture. To address this, we propose a novel Differential-Feature Dynamic-Fusion Network (DF2Net), which aims to improve the accuracy of building change detection in remote sensing images. DF2Net achieves efficient detection of change areas through the collaborative design of three key modules: Differential-Feature Frequency-domain Attention Module (DFAM), Global-Local Feature Dynamic-Fusion Module (GFDM), and Pseudo-label Deep-supervision Module (PDM). Specifically, DFAM combines Fourier transform and self-attention mechanisms to extract key information from the frequency domain, thereby improving the feature representation of change areas. GFDM dynamically fuses global and local features to effectively capture multi-scale change information. PDM optimizes feature learning in the middle layer of the decoder by introducing pseudo-label supervision. Experimental results on three mainstream building change detection datasets, LEVIR-CD, WHU-CD, and HRCUS-CD, show that DF2Net significantly outperforms existing mainstream methods in both F1-score and IoU, demonstrating superior performance. This indicates that DF2Net has broad application prospects in handling complex scenarios and multi-scale change detection tasks. The code available from https://github.com/Mw-yang/DF2Net.