TCCFNet: a semantic segmentation method for mangrove remote sensing images based on two-channel cross-fusion networks by Lixiang Fu, Yaoru Wang, Shulei Wu, Jiasen Zhuang, Zhongqiang Wu, Jian Wu, Huandong Chen, Yukai Chen

“…However, due to the complexity of mangrove distribution and the similarity among different categories in remote sensing images, traditional image segmentation methods struggle to accurately identify mangrove regions. …”
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Radiomics as a new frontier in modern rheumatology: Chest pathology visualization advances and prospects by T. V. Beketova, E. L. Nasonov, M. A. Alekseev, E. I. Shchepikhin, Yu. N. Philippovich, A. S. Kruzhalov, A. Yu. Philippovich, V. A. Kulbak, D. A. Argunova, P. G. Shakhnovich, T. A. Prazdnichnykh, M. P. Obidin, T. N. Krasnova, N. N. Vladimirova

“…The article discusses the modern trends in the development of digital technologies in medicine, exemplified by rheumatology, especially, the significance of radiomics, which combines radiology, mathematical modeling, and deep machine learning. Texture analysis of computed tomography images and other imaging methods provides a more deeply characterization of the pathophysiological features of tissues and can be considered as a non-invasive “virtual biopsy”.It is shown that radiomics enhances the quality of diagnostic and predictive modeling. …”
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Interferometric Synthetic Aperture Radar Phase Linking with Level 2 Coregistered Single Look Complexes: Enhancing Infrastructure Monitoring Accuracy at Algeciras Port by Jaime Sánchez-Fernández, Alfredo Fernández-Landa, Álvaro Hernández Cabezudo, Rafael Molina Sánchez

“…The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (Coregistered Multi-temporal Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach implemented in MiaplPy. …”
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Image optimization method for ablation defects in high-voltage cable buffer layers based on X-ray detection technique by Baiyuan Liu, Weifeng Wang, Tengfei Liu, Zhen Xu, Xiangchen Kong, Xu Zhou, Yanan Cheng

“…It combines multi-scale analysis with directional constraints to reduce false detections in complex backgrounds and utilizes Lanczos3 interpolation to reconstruct defect edge textures with high fidelity while suppressing ringing artifacts. …”
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Clinical and imaging features of co-existent pulmonary tuberculosis and lung cancer: a population-based matching study in China by Fan Zhang, Fei Qi, Yi Han, Hongjie Yang, Yishuo Wang, Guirong Wang, Yujie Dong, Hongxia Li, Yuan Gao, Hongmei Zhang, Tongmei Zhang, Liang Li

“…Additionally, in comparison to LC alone, patients with IAPTB-LC exhibited more complex symptoms, imaging features, and lymph node metastases. …”
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Building rooftop extraction from high resolution aerial images using multiscale global perceptron with spatial context refinement by Qinglie Yuan

“…Automatic building detection and extraction algorithms using high spatial resolution aerial images can provide precise location and geometry information, significantly reducing time, costs, and labor. …”
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MAHE: a multiscale and hybrid expert-based model for image-text enhanced named entity recognition on social media by Song Yang, Baolin Mo, Dayang Liu, Liangkuan Zhu

“…This approach often struggles to accurately align text with semantic understanding of images in complex network environments. To address this issue and improve both the accuracy and efficiency of identity verification, this paper proposes a novel framework: an MNER model based on the joint effect of multi-scale Mamba and a hybrid expert mechanism for modality enhancement. …”
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Typical Crop Classification of Agricultural Multispectral Remote Sensing Images by Fusing Multi-Attention Mechanism ResNet Networks by Zongpu Li, Zhiyun Xiao, Yulong Zhou, Tengfei Bao

“…This model leverages multispectral remote sensing images from unmanned aerial vehicles (UAVs) to accurately classify complex crop planting structures. …”
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Exploring the feasibility of integrating ultra‐high field magnetic resonance imaging neuroimaging with multimodal artificial intelligence for clinical diagnostics by Yifan Yuan, Kaitao Chen, Youjia Zhu, Yang Yu, Mintao Hu, Ying‐Hua Chu, Yi‐Cheng Hsu, Jie Hu, Qi Yue, Mianxin Liu

“…Conclusions In general, our analysis suggests that such integration has promise as a tool to improve the workflow of diagnostics in neurology, with a potentially transformative impact in the fields of medical image analysis and patient management.…”
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Effect of flow diverter stent malposition on intracranial aneurysm hemodynamics-An experimental framework using stereoscopic particle image velocimetry. by Christoph Roloff, Philipp Berg

“…For the assessment of the individual flow modifications, high-resolution stereoscopic particle image velocimetry (PIV) measurements were carried out.…”
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A Novel Survey for Young Substellar Objects with the W-band Filter. VII. Water-bearing Objects in the Core of the ρ Ophiuchi Cloud Complex by Tanvi Sharma, Wen Ping Chen, Beth Biller, Loïc Albert, Belinda Damian, Jessy Jose, Bhavana Lalchand, Michael C. Liu, Yumiko Oasa

“…Brown dwarf candidates too faint for membership determination by Gaia have their proper motions derived by deep-infrared images spanning 6 yr. Astrometric analysis confirms 21/22 sources as members, with one identified as a contaminant. …”
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Improving the early diagnosis and clinical outcomes of shock patients via laser speckle contrast imaging assessment of peripheral hemodynamics by Meng-Che Hsieh, Jin-Jia Hu, Yan-Ren Lin, Shih-Yu Li, Pei-You Hsieh, Congo Tak Shing Ching, Lun-De Liao

“…These findings demonstrate the potential of LSCI to improve diagnosis and management of shock, especially in complex clinical environments.…”
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CAEB7-UNet: An Attention-Based Deep Learning Framework for Automated Segmentation of C-Spine Vertebrae in CT Images by Abhishek Kumar Pandey, Kedarnath Senapati, G. P. Pateel

“…Accurate segmentation of vertebrae in computed tomography (CT) images possess serious challenges due to the irregular vertebral boundaries, low contrast and brightness, and noise in CT scans. …”
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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution by Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song

“…Abstract High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. …”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

“…Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. …”
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Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation by Lemin Shi, Ping Gong, Mingye Li, Dianxin Song, Hao Zhang, Zhe Wang, Xin Feng

“…This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acquisition and signal enhancement framework. …”
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Dual-Attention-Enhanced MobileViT Network: A Lightweight Model for Rice Disease Identification in Field-Captured Images by Meng Zhang, Zichao Lin, Shuqi Tang, Chenjie Lin, Liping Zhang, Wei Dong, Nan Zhong

“…In this study, we constructed an image dataset containing six classes of rice diseases captured under real field conditions to address challenges such as complex backgrounds, varying lighting, and symptom similarities. …”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

“…To address this challenge, this study presents a novel approach for the classification and visualization of rock microstructure from micro-computed tomography images, leveraging pre-trained convolutional neural network (CNN) models (AlexNet, GoogLeNet, Inception v3 Net, ResNet, and DenseNet) combined with unsupervised machine learning (USML) techniques principal component analysis, multidimensional scaling, isometric mapping, t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation projection (UMAP)). …”
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Role of artificial intelligence in imaging and endoscopy for the diagnosis, monitoring and prognostication of inflammatory bowel disease: a scoping review protocol by Mallory Chavannes, Bram Verstockt, Mariangela Allocca, David T Rubin, Kerri Novak, Lynn Kysh, Noa Krugliak Cleveland, Michael Todd Dolinger, Tom S Robbins, Shintaro Sagami

“…Their assessment and monitoring are becoming increasingly important, though complex. The best disease control is achieved through tight monitoring of objective inflammatory parameters (such as serum and stool inflammatory markers), cross-sectional imaging and endoscopic assessment. …”
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Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework by Lei-Si Hu, Jie Wang, Heng-Ming Zhang, Hai-Yu Huang

“…A dataset consisting of 3,388 ultra-widefield (UWF) fundus images obtained from patients with DR, each with a resolution of 2,600 × 2048 pixels, was utilized for both training and testing purposes. …”
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