Suchergebnisse - 3D encoder-decoder architecture*

A novel hybrid layer-based encoder–decoder framework for 3D segmentation in congenital heart disease

Autoren: Yaoxi Zhu Hongbo Li Bingxin Cao et al.

Quelle: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-10 (2025)

Schlagwörter: Heart Defects, Congenital, Science, Deep learning, Heart, Hybrid architectures, Article, Imaging, Three-Dimensional, Deep Learning, 3D CT image, Image Processing, Computer-Assisted, Medicine, Humans, Algorithms, Congenital heart disease, Cardiac segmentation

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40195399
https://doaj.org/article/bf5a61cd628a48edacb57316310c0da4

SISR of Hyperspectral Remote Sensing Imagery Using 3D Encoder-Decoder RUNet Architecture

Autoren: Aburaed, Nour Alkhatib, Mohammed Q. Marshall, Stephen et al.

Quelle: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. :1516-1519

Schlagwörter: Electrical engineering. Electronics Nuclear engineering, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dateibeschreibung: text

ST-3DView: Multi-Scale Contrast-Enhanced 3D Point Cloud Reconstruction of Single-View Objects From Video Scene Transition

Autoren: Dipanita Chakraborty Werapon Chiracharit Kosin Chamnongthai

Quelle: IEEE Access, Vol 13, Pp 69596-69618 (2025)

Schlagwörter: 3D neural network, 3D reconstruction, object segmentation, Electrical engineering. Electronics. Nuclear engineering, scene transition detection, encoder-decoder architecture, point cloud, TK1-9971

Zugangs-URL: https://doaj.org/article/9fc22eed249e49a7aa1ba7cd65bced3f

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

Autoren: Shakya, Mahesh Khanal, Bishesh

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2309.13587

Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation

Autoren: Hai Nguyen-Truong Quan-Dung Pham

Quelle: Lecture Notes in Computer Science ISBN: 9783031090011

Encoder-Decoder Architecture for 3D Seismic Inversion

Autoren: Gelboim, Maayan Adler, Amir Sun, Yen et al.

Index Begriffe: Physics - Geophysics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, text

URL: http://arxiv.org/abs/2207.14789

Encoder–Decoder Architecture for 3D Seismic Inversion

Autoren: Maayan Gelboim Amir Adler Yen Sun et al.

Quelle: Sensors (Basel)
Sensors, Vol 23, Iss 1, p 61 (2022)
Sensors; Volume 23; Issue 1; Pages: 61

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, inverse problems, Chemical technology, seismic velocity, deep learning, FOS: Physical sciences, TP1-1185, 02 engineering and technology, transfer learning, seismic inversion, 01 natural sciences, Article, Geophysics (physics.geo-ph), Machine Learning (cs.LG), 0104 chemical sciences, Physics - Geophysics, FOS: Electrical engineering, electronic engineering, information engineering, 3D reconstruction, encoder–decoder, Electrical Engineering and Systems Science - Signal Processing, 0210 nano-technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36616658
http://arxiv.org/abs/2207.14789
https://doaj.org/article/a47ec4a216ee41f5949792bfa0f45c5f

A novel lightweight 3D CNN for accurate deformation time series retrieval in MT-InSAR

Autoren: Mahmoud Abdallah Xiaoli Ding Samaa Younis et al.

Quelle: Science of Remote Sensing, Vol 11, Iss , Pp 100206- (2025)

Schlagwörter: MT-InSAR, Deformation retrieval, Lightweight, 3D encoder-decoder architecture, Separable CNN, Physical geography, GB3-5030, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666017225000124; https://doaj.org/toc/2666-0172

Zugangs-URL: https://doaj.org/article/7ce5e628f5fa40d1a5f2babbb8155481

Whole Heart Segmentation Using 3D FM-Pre-ResNet Encoder–Decoder Based Architecture with Variational Autoencoder Regularization

Autoren: Marija Habijan Irena Galić Hrvoje Leventić et al.

Quelle: Applied Sciences, Vol 11, Iss 9, p 3912 (2021)

Schlagwörter: artificial intelligence, cardiac CT, cardiac MRI, deep learning, ResNet, variational autoencoder, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/9/3912; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/ffa6dc8320a8424e99d6f30ff091b71b

H-Net: A Multitask Architecture for Simultaneous 3D Force Estimation and Stereo Semantic Segmentation in Intracardiac Catheters

Autoren: Pedram Fekri Mehrdad Zadeh Javad Dargahi

Quelle: IEEE Robotics and Automation Letters. 10:844-851

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Robotics, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Robotics (cs.RO), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2501.00514

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging

Autoren: Asim Zaman Haseeb Hassan Xueqiang Zeng et al.

Quelle: Front Neurosci
Frontiers in Neuroscience, Vol 18 (2024)

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, adaptive feature extraction, brain lesion segmentation, computer-aided diagnosis, Neurosciences. Biological psychiatry. Neuropsychiatry, attention mechanism, medical image analysis, encoder-decoder architecture, RC321-571, Neuroscience

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38680446
https://doaj.org/article/25c8d1842e5644808d16dfc43ffba1af

H-Net: A Multitask Architecture for Simultaneous 3D Force Estimation and Stereo Semantic Segmentation in Intracardiac Catheters

Autoren: Fekri, Pedram Zadeh, Mehrdad Dargahi, Javad

Index Begriffe: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics, text

URL: http://arxiv.org/abs/2501.00514

Leveraging bidirectional long short-term memory in a encoder-decoder architecture to fuse inter-slice information for enhancing burn tissue segmentation using optical coherence tomography.

Autoren: Dhiman, Babita Kamboj, Sangeeta Srivastava, Vishal

Quelle: Biomedical Signal Processing & Control. Sep2025, Vol. 107, pN.PAG-N.PAG. 1p.

Schlagwörter: Long short-term memory, Machine learning, Optical coherence tomography, Surgeons, Deep learning

Multi stage mesh attention UNet architecture for 3D dental segmentation.

Autoren: Mouncif, Hamza Kassimi, Amine Gardelle, Thierry Bertin et al.

Quelle: Discover Artificial Intelligence; 11/24/2025, Vol. 5 Issue 1, p1-17, 17p

Schlagwörter: DATA augmentation, DENTISTRY, ORTHODONTICS, ARTIFICIAL neural networks, MORPHOLOGY

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling

Autoren: Ji, Hangyu

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing

Zugangs-URL: http://arxiv.org/abs/2506.05297

Hierarchical 3D Feature Learning forPancreas Segmentation

Autoren: Federica Proietto Salanitri Concetto Spampinato Ulas Bagci et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783030875886

Schlagwörter: Segmentation map, FOS: Computer and information sciences, Computer Science - Machine Learning, Decoding, Convolutional neural network, 02 engineering and technology, 3D modeling, Machine Learning (cs.LG), Medical computing, 03 medical and health sciences, Magnetic resonance imaging, 0302 clinical medicine, Network coding, Statistical tests, Learn+, Machine learning, MRI scan, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Hierarchical encoder-decoder architecture, Encoder-decoder architecture, CT and MRI pancreas segmentation, Image and Video Processing (eess.IV), Feature learning, Fully convolutional neural networks, CT-scan, Electrical Engineering and Systems Science - Image and Video Processing, Computerized tomography, Convolution, Convolutional neural networks, Medical imaging, Fully convolutional neural network

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36780259
http://arxiv.org/abs/2109.01667
https://link.springer.com/chapter/10.1007/978-3-030-87589-3_25
https://dblp.uni-trier.de/db/journals/corr/corr2109.html#abs-2109-01667

Deep Learning With Noisy Labels for Spatiotemporal Drought Detection

Autoren: Cortés-Andrés, Jordi Fernández-Torres, Miguel-Ángel Camps-Valls, Gustau et al.

Weitere Verfasser: Cortés-Andrés, Jordi Fernández-Torres, Miguel-Ángel Camps-Valls, Gustau et al.

Quelle: e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
Universidad Carlos III de Madrid (UC3M)
IEEE Transactions on Geoscience and Remote Sensing

Schlagwörter: Informática, Noise measurement, Drought detection, Data models, Biological system modeling, Convolutional neural networks (CNNS), Droughts, Predictive models, Europe, Label correction (LC), Noisy labels (NLs), Medio Ambiente, Solid modeling, Training, Three-dimensional displays, Spatiotemporal data, Encoder-decoder architecture, Noise, Hydro-climatological data

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/10016/46966

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Autoren: Weichao Wu Zhong Xie Yongyang Xu et al.

Quelle: Remote Sensing, Vol 13, Iss 23, p 4917 (2021)

Schlagwörter: 3D point clouds, shape completion, deep learning, multi-view-based methods, Science

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2072-4292/13/23/4917; https://doaj.org/toc/2072-4292

Zugangs-URL: https://doaj.org/article/58e500052cdc4813bfea607a381de9f5

A Multi-Resolution Attention U-Net for Pavement Distress Segmentation in 3D Images: Architecture and Data-Driven Insights.

Autoren: Gong, Haitao Tao, Jueqiang Luo, Xiaohua et al.

Quelle: Mathematics (2227-7390); Sep2025, Vol. 13 Issue 17, p2752, 18p

Schlagwörter: IMAGE segmentation, DEEP learning, SURFACE cracks, THREE-dimensional imaging, DETECTION algorithms, FEATURE extraction

Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder

Autoren: Lin, Xiuchun Liu, Yichao Feng, Chen et al.

Quelle: Lin, X, Liu, Y, Feng, C, Chen, Z, Yang, X & Cui, H 2024, 'Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder', Electronics (Switzerland), vol. 13, no. 10, 1813. https://doi.org/10.3390/electronics13101813

Schlagwörter: 3D convolution, automatic evaluation method, encoder–decoder, functional movement screening, human movement feature

Dateibeschreibung: application/pdf

Verfügbarkeit: https://research.monash.edu/en/publications/35627eab-d5ed-4a3f-bbbe-e5f18d39d46e
https://doi.org/10.3390/electronics13101813
https://researchmgt.monash.edu/ws/files/616959899/599091885-oa.pdf
http://www.scopus.com/inward/record.url?scp=85194265801&partnerID=8YFLogxK