In the field of medical image analysis, accurate classification of images is crucial for diagnosing diseases and formulating treatment plans. Many studies have shown that global features and local features help reduce noise interference in medical images. Due to the fixed receptive field size of the convolution kernel, it is difficult to capture the global features of the image. Although the self-attention-based Transformer can model long-range dependencies, it has high computational complexity and lacks local inductive bias. This paper proposes a new module based on dual attention, the Dual Attention Residual Module(DARNet), which uses multi-head self-attention (MHSA) to enhance the global feature extraction ability of convolutional neural network (CNN), while the convolutional block attention module (CBAM) enhances local feature extraction, and then fuses features at different levels through dual-step attention fusion (DSAF). Then we use the architecture of resnet to build the network-Dual Attention Residual Network (DARNet). We evaluate our network on the ISIC2018 and Kvasir datasets and demonstrate its superior performance compared with state-of-the-art models

Dynamic range compression of high radiometric resolution remote sensing images involves compressing the grayscale value range to 0∼255. This process is a necessary preprocessing step for the visualization, storage, and analysis of remote sensing images. This task falls under tone mapping, but due to data heterogeneity and variations in semantic representation, existing tone mapping methods often result in color distortion and visual artifacts when applied to remote sensing images. We propose an unsupervised dynamic range compression method that leverages the characteristics of remote sensing images and tasks. In the construction of the generator, we drew an analogy between the pixel values during the compression process and the particle motion within a closed thermal field. Utilizing the thermodynamic difference equation, we developed a third-order finite difference residual module to explicitly guide the model’s feature extraction. Considering the limitations of the existing contrastive loss in terms of attention range, we propose a multi-granularity contrastive loss that operates at both the patch and semantic levels. Additionally, based on the similarity of histogram shapes before and after dynamic range compression, we introduce a histogram shape context similarity loss to regulate the image color distribution. Due to the limited number of existing studies, we have constructed a dataset and conducted extensive experimental verifications. The results indicate that the proposed method yields superior outcomes and is applicable to downstream tasks. The relevant code and dataset can be accessed via the following link: https://github.com/ZzzTD/RS_DRC .

The incidence of multi-drug resistant bacterial species is rapidly increasing. To avoid antibiotic misuse and further exacerbation of this health crisis, clinicians should obtain a swift and accurate diagnosis of bacterial resistance. Existing laboratory-based antibiotic susceptibility testing (AST) methods are slow and laborious. Our study puts forward an efficient end-to-end image classification network using deep learning for predicting antibiotic resistance from fluorescence microscopy images of bacteria. Our resulting PAS-Net model integrates two parallel branches: the convolution branch (C-branch), employing ConvNeXt, and the Transformer branch (T-branch), utilizing Vision Transformer (ViT). To bridge these branches effectively, we introduce the feature interaction unit (FIU), which facilitates the integration of local features generated by the C-branch and global representations by the T-branch interactively. Additionally, we present a novel attention mechanism which reduces computational costs while maintaining the global representation capability. Experimental results, conducted on a fluorescence image dataset and public open datasets, suggest the promising potential of our method for rapidly diagnosing antibiotic susceptibility in bacteria and achieving high prediction performance. The code is publicly available at:https://github.com/Td270/PAS-NetNote to Practitioners—The rise of multi-drug resistant bacteria poses a significant challenge to public health, requiring faster and more accurate methods to predict bacterial antibiotic susceptibility. Current laboratory methods are slow and often impractical for time-sensitive clinical decisions. This work introduces PAS-Net, a deep learning framework designed to predict antibiotic resistance directly from fluorescence microscopy images of bacteria. Practitioners can apply this method in clinical and laboratory settings to significantly reduce diagnosis time, potentially enabling real-time decision-making. By combining convolutional and Transformer-based architectures, PAS-Net captures both detailed local patterns and global structural information, leading to improved predictive accuracy. This method is particularly suited for labs with access to fluorescence imaging equipment but limited by the slow throughput of traditional assays. However, the approach requires high-quality imaging and computational resources, which could limit its immediate application in low-resource settings. Future research should focus on adapting the model for broader imaging modalities and reducing its dependency on computational infrastructure. Beyond clinical diagnostics, the approach could be extended to other microscopy-based applications, such as microbial ecology or industrial bioprocess monitoring, where rapid and accurate phenotypic assessments are required.

To address the challenges posed by diverse water body morphologies, irregular boundaries, and significant reflectance variations in remote sensing images—factors often overlooked by general-purpose models that lead to local omissions, boundary mislocalizations, and false detections—we propose a novel Complex Water Body Segmentation Network (CWBSNet). This network is designed for water body extraction tasks on both the Sentinel-2 dataset S1S2-Water and the aerial dataset FLAIR#1. CWBSNet employs a Transformer-based backbone to generate four-level feature embeddings with long-range global dependencies. To enhance global water body representation and mitigate local omission issues, we introduce the Spectrum Transform Module (STM), which exchanges spectral information between water and non-water regions on small-scale features via frequency-domain transformations. Furthermore, we design a Multi -level Feature Extraction and Fusion Module (MFEFM), comprising Block1, a Spatial -channel Information Fusion Block (SIFB), and Block3, which enhances local feature representations through attention mechanisms, multi-scale convolutions, and spatial-channel transformations. Directional convolutions are incorporated into shallow features to capture orientation cues and refine boundary detail representation. In addition, based on the strong response of Near-Infrared (NIR) and Normalized Difference Water Index (NDWI) to water bodies, we propose the Information Augmentation Module (IAM). This module integrates Rotary Position Embedding (ROPE) Attention into a Linear Attention Transformer to support water body recognition and reduce false positives. Finally, a decoder generates the saliency map. Experiments on two public datasets demonstrate that CWBSNet achieves superior performance in both training efficiency and detection accuracy compared to state-of-the-art methods. The code is available at: https://github.com.

The majority of current remote sensing images possess high-radiometric resolution exceeding 10 bits. Precisely compressing this radiometric resolution to 8 bits is crucial for visualization and subsequent deep learning tasks. Previously, radiometric resolution compression required extensive parameter adjustments of traditional tone mapping operators. Deep learning is gradually replacing this high manual dependency method. However, existing deep learning tone mapping techniques are primarily designed for natural scene images captured by digital cameras, making direct application to remote sensing images challenging. This limitation stems from disparities in data formats and the complexity of semantic representation in remote sensing images. Moreover, the block prediction inherent in deep learning models often results in tiling artifacts post-splicing, failing to satisfy the scale dependency of remote sensing images. To tackle these challenges, we propose leveraging contrastive learning methods to compress the radiometric resolution of remote sensing images. Given the rich detail information and complex spatial distribution of objects in remote sensing images, we develop a CNN-Transformer hybrid generator capable of capturing both local details and long-range dependencies. Building upon this, we introduce non-local self-similarity contrastive loss and histogram similarity loss to enhance feature expression and regulate image color distribution. Additionally, we present a post-processing technique based on hybrid histogram matching to enhance image quality and seamlessly generate whole-scene images. Through experiments and comparisons on our dataset, our method demonstrates superior performance. The dataset and code can be obtained online in this link https://github.com/ZzzTD/RRCGAN.

The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level semantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic SLAM termed HSS-SLAM. We incorporate human-in-the-loop into our method, establishing an interaction module to facilitate human editing and rectifying semantic information. Additionally, to minimize the manual correction workload, a lightweight and intuitive method for semantic extension is proposed, augmenting the semantic richness of the global map with a few operations. Furthermore, our method adopts superquadrics for object representation, enabling detailed descriptions of various object shapes. This mitigates the limitation of conventional semantic mapping, where objects are difficult to distinguish due to the reliance on a single-shape representation. Subsequently, precise estimation of superquadric parameters and camera poses is achieved through joint optimization. Extensive experiments conducted on TUM RGB-D and Scenes V2 datasets demonstrate that the proposed approach exhibits competitive performance, surpassing current methods in both object representation and camera localization accuracy.

Quickly and accurately obtaining street lamp post information has great application value in smart city construction and automatic vehicle navigation. However, the existing deep learning methods are affected by factors such as the perspective effect, different objects with the same spectrum, and occlusion. There can also be some problems in the semantic segmentation results for street lamp posts, such as under-segmentation, misextraction, and discontinuity. In this paper, we present the OSLPNet model for the extraction of street lamp posts from street view imagery. According to the characteristics of the various scales of street lamp posts in the imagery, a multi-scale phased controller (MPC) with multi-level receptive fields is proposed to reduce the under-segmentation problem for street lamp posts. According to the unique “elbow” structure of street lamp posts, deformable convolution is introduced to reduce the problem of misextraction of street lamp posts. According to the topological relationship of street lamp post context, a lightweight spatial context (LSC) module is proposed to solve the problem of discontinuous detection of street lamp posts caused by occlusion. We also proposed two street lamp pole datasets, and experimental results showed that our F1 values can reach 85.2% and 82.4% under both datasets, which is superior to the existing state of art method. The code and datasets are publicly available at https://github.com/ZzzTD/OSLPNet.