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Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation.
Sensors (Basel)
December 2024
Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps.
View Article and Find Full Text PDFA Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities.
Sensors (Basel)
December 2024
2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection.
View Article and Find Full Text PDFLarge-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy.
Plants (Basel)
December 2024
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages.
View Article and Find Full Text PDFEvaluating Neoadjuvant Immunochemotherapeutic Response for Bladder Carcinoma Using Amide Proton Transfer-Weighted MRI.
Acad Radiol
January 2025
Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, PR China (L.K., B.W., Q.C., L.M., W.C., Y.C., Y.G., H.W.). Electronic address:
Rationale And Objectives: To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted MRI in evaluating the response of bladder cancer (BCa) to neoadjuvant immunochemotherapy.
Materials And Methods: From June 2021 to July 2023, participants with pathologically confirmed BCa were prospectively recruited to undergo MRI examinations, including APTw and diffusion-weighted MRI before and after neoadjuvant immunochemotherapy. Histogram analysis features (mean, median, and entropy) were extracted from pre- and post-treatment APTw and apparent diffusion coefficient (ADC) maps, respectively.
Fusion of FDG and FMZ PET Reduces False Positive in Predicting Epileptogenic Zone.
AJNR Am J Neuroradiol
January 2025
From the School of Biomedical Engineering (B.C., H.H., J.L., S.Y., Y.C., J.L.), Shanghai Jiao Tong University, Shanghai, China; Department of Neurosurgery (S.J., J.H., L.C.), and PET Center (W.B.), Huashan Hospital, Fudan University, Shanghai, China.
Background And Purpose: Epilepsy, a globally prevalent neurological disorder, necessitates precise identification of the epileptogenic zone (EZ) for effective surgical management. While the individual utilities of FDG PET and FMZ PET have been demonstrated, their combined efficacy in localizing the epileptogenic zone remains underexplored. We aim to improve the non-invasive prediction of epileptogenic zone (EZ) in temporal lobe epilepsy (TLE) by combining FDG PET and FMZ PET with statistical feature extraction and machine learning.
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