Hyperspectral imaging combined with residual-attention-net for spectral-spatial feature fusion in liver disease diagnosis by Yunze Li, Jingjing Wang, Miaoqing Zhao, Jinlin Deng, Chongxuan Tian, Qize Lv, Yifei Liu, Kun Ru, Wei Li

“…This study aims to propose a novel hyperspectral image (400–1000 nm) processing method based on 3D-Residual-attention networks (3D Ra-Net) to improve the accuracy of differentiation between the two.The study employs a 3D Ra-Net model that integrates spectral features with spatial information to enhance classification accuracy. …”
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Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models by Hangxiu Liu, Youyou Wang, Yiheng Wang, Jingyi Wang, Hanqing Hu, Xinyi Zhong, Qingjun Yuan, Jian Yang

“…Convolutional neural networks (CNNs) were developed and compared with conventional machine learning models, including partial least-squares discriminant analysis (PLS-DA), support vector machines (SVMs), and a multilayer perceptron (MLP). …”
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A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

“…Abstract The exponential growth of Big Data in healthcare, particularly in AI-driven medical diagnostics, has raised critical concerns about data privacy in medical image classification. …”
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Distinct functional cerebral hypersensitivity networks during incisional and inflammatory pain in rats by Silke Kreitz, Bruno Pradier, Daniel Segelcke, Saeedeh Amirmohseni, Andreas Hess, Cornelius Faber, Esther M. Pogatzki-Zahn

“…We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. …”
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A Predictive Method for Unplanned Postoperative Readmission Risk Based on Heterogeneous Data by Kai YU, Xiaofeng DONG, Zhenming YUAN, Zhaojian CUI, Weibin LUO

“…Additionally, the model’s predictive performance could be improved by integrating medical imaging data, such as CT scans and X-rays, once they are properly fused with other data types using the CTFN approach.…”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. …”
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Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms by Qiao Chang, Yuxing Bai, Shaofeng Wang, Fan Wang, Shuang Liang, Xianju Xie

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Verifying Robustness of Neural Networks in Vision-Based End-to-End Autonomous Driving by Cinzia Bernardeschi, Giuseppe Lami, Francesco Merola, Federico Rossi

“…The main contributions of this work are: i) We provide a comprehensive analysis of current neural-network-based perception and decision-making components in autonomous vehicles, highlighting their susceptibility to attacks or perturbation. ii) We develop and implement a novel framework for systematically evaluating and verifying the robustness of neural networks against such perturbations, focusing on imperceptible image modifications. iii) We present empirical results demonstrating our verification framework’s effectiveness in identifying weaknesses and providing probabilistic guarantees on network behavior. …”
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Computer Vision for Substrate Detection in High‐Throughput Biomaterial Screens Using Bright‐Field Microscopy by Robert Owen, Aishah Nasir, Mahetab H. Amer, Chenxue Nie, Xuan Xue, Laurence Burroughs, Chris Denning, Ricky D. Wildman, Faraz A. Khan, Morgan R. Alexander, Felicity R. A. J. Rose

“…Herein, two case studies demonstrate how open‐source approaches, (region‐based convolutional neural network and algorithmic [OpenCV]), can be integrated into cell‐biomaterial HTS analysis to automate bright‐field segmentation across thousands of images, allowing rapid, spatial definition of biomaterial features during cell analysis for the first time.…”
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Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning by Cheng-Shun Lee, Jenn-Jier James Lien, Kai Chain, Li-Chun Huang, Zhong-Wei Hsu

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Images of I. V. Stalin and A. Hitler in Historical Memory: Constructing the Past in the Media Space by Denis S. Artamonov, Sergei P. Sidorov, Sophia V. Tikhonova

“…The use of quantitative analysis data to study the images of I. Stalin and A. …”
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EST-STFM: An Efficient Deep-Learning-Based Spatiotemporal Fusion Method for Remote Sensing Images by Qiyuan Zhang, Xiaodan Zhang, Chen Quan, Tong Zhao, Wei Huo, Yuanchen Huang

“…Spatiotemporal fusion methods address the limitation that a single satellite cannot simultaneously provide high spatial and temporal resolution imagery. By integrating images with different spatial and temporal characteristics, it is possible to generate remote sensing data with enhanced detail and frequency. …”
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Text-Conditioned Diffusion-Based Synthetic Data Generation for Turbine Engine Sensor Analysis and RUL Estimation by Luis Pablo Mora-de-León, David Solís-Martín, Juan Galán-Páez, Joaquín Borrego-Díaz

“…The approach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized signals into three components, mapped to the RGB channels of an image. …”
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Real-time quantification of activated sludge concentration and viscosity through deep learning of microscopic images by Hewen Li, Yu Tao, Tiefu Xu, Hongcheng Wang, Min Yang, Ying Chen, Aijie Wang

“…Traditional methods for determining these parameters are time-consuming, require separate measurements for each index, and fail to provide real-time data for future ‘smart’ WWTPs. Here we show a real-time online microscopic image data analysis system that quantitatively identifies MLSS and apparent viscosity. …”
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Signal Compensation of Moon Mineralogy Mapper (M3) Under Low-Illumination Conditions Using a CycleGAN-Based Network by Rui Ni, Fei Zhao, Tingyu Meng, Yanlei Du, Pingping Lu, Robert Wang

“…The efficacy of the proposed method is validated using real high SNR M3 spectral observations, with the performance of the compensated results comprehensively assessed across three dimensions: structural indicators, spectral indices, and spatial consistency analysis. The strong correlation between the M3 spectral compensation results with Selenological Engineering Explorer (Kaguya) multiband imager data, as well as other sensors' inversion of plagioclase abundance around the Shackleton Crater, underscores the network's potential for mineral exploration. …”
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SynAdult: Multimodal Synthetic Adult Dataset Generation via Diffusion Models and Neuromorphic Event Simulation for Critical Biometric Applications by Muhammad Ali Farooq, Paul Kielty, Wang Yao, Peter Corcoran

“…We propose SynAdult, a multimodal synthetic data generation framework designed to address the scarcity of diverse and privacy-compliant senior adult face datasets for biometric applications and facial analysis. …”
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CUNet-CLSTM: A Novel Fusion of CUNet and CLSTM for Superior Liver Cancer Detection in CT Scans by K. Vijayaprabakaran, Padmanaban Ramalingam, Rajakumar Ramalingam, A. Ilavendhan, R. Vedhapriyavadhana

“…Computed tomography (CT) scans are crucial in the diagnosis of liver cancer, providing detailed images for medical analysis. Despite the triumph of convolutional neural networks in medical image analysis, challenges such as overfitting, limited labeled data, and complex tumor morphology hinder accurate detection of liver cancer. …”
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Research on Change Information Retrieval Method for Energy Infrastructure Based on Optical Remote Sensing Images by Zhibao Wang, Ying Yuan, Man Zhao, Dan Zhou, Lu Bai, Jinhua Tao, Anna Jurek-Loughrey

“…By integrating both approaches, change information retrieval allows for identifying images with features similar to those in a query time-series from vast archives of temporal data. …”
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Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques. by Apichat Suratanee, Panita Chutimanukul, Tanapon Saelao, Supachitra Chadchawan, Teerapong Buaboocha, Kitiporn Plaimas

“…Traditional methods for antioxidant analysis in holy basil (Ocimum tenuiflorum L.) are time-consuming, while hyperspectral imaging has the potential to rapidly observe holy basil. …”
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ANALYSIS OF REMOTE SENSING DATA PERTAINING TO DEBRIS FLOWS: INSIGHTS FROM SELECTED DRAINAGE BASINS IN BULGARIA by Ilia Tamburadzhiev

“…Morphometric parameters and the Normalized Difference Vegetation Index (NDVI) were calculated in Geographic Information System (GIS) environment. The channel networks were classified by stream ordering. GIS analysis is done on the basis of Digital Elevation Model (DEM), Landsat multispectral satellite images, and geological maps. …”
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