Suchergebnisse - "U-Net"

AI-Augmented 3D Craniofacial Reconstruction for Enhanced Surgical Planning: A Novel Integration of Depth-Augmented Vision Transformers and MeshCNN for Structural Fidelity

Autoren: Vishal, n. Kukreja, V. Ahuja, V. et al.

Quelle: Egyptian Informatics Journal. 32

Schlagwörter: 3D U-net segmentation, AI-augmented pre-surgical planning, Craniosynostosis reconstruction, Depth-Augmented Vision Transformers

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-55894
https://doi.org/10.1016/j.eij.2025.100810

Enhanced glaucoma detection using U-Net and U-Net plus architectures using deep learning techniques

Autoren: Kumar, B. P. Pradeep Rangaiah, Pramod K.B. Augustine, Robin

Quelle: Wireless Brain-Connect inteRfAce TO machineS: B-CRA TOS, European Union’s Horizon 2020 Research and Innovation Program, Grant agreement ID: 965044 “BOS: Software Principles & Techniques for a Body-centric OS”, the Swedish Foundation for Strategic Research (SSF) grant FUS Photodiagnosis and Photodynamic Therapy. 54

Schlagwörter: Glaucoma, Retinal intra-ocular region, Frame networks, U-NET layers, DRISHTI-GS, DRIONS-DB, HRF

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-567054
https://doi.org/10.1016/j.pdpdt.2025.104621

Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach

Autoren: Andersson, Peter M Båth, Magnus Palm, Åsa et al.

Quelle: Biomedical Engineering & Physics Express. 11(3)

Schlagwörter: Algorithms, Computer Simulation, Deep Learning, Humans, Image Processing, Computer-Assisted, Monte Carlo Method, Neoplasms, Particle Accelerators, Phantoms, Imaging, Radiotherapy Dosage, Radiotherapy Planning, Radiotherapy, Intensity-Modulated, Failure analysis, Gamma rays, Hadrons, Photons, Electronic portal imaging device dosimetry, Electronic portal imaging devices, Energy fluences, Monte carlo, Monte Carlo’s simulation, Radiotherapy treatment, Simulation, Transmission dosimetry, Treatment plans, Article, cancer radiotherapy, comparative study, deep learning, feasibility study, gamma radiation, human, in vivo dosimetry, intensity modulated radiation therapy, Monte Carlo method, prediction, radiation beam, radiation dose distribution, radiation energy, radiotherapy dosage, treatment planning, U-Net architecture, volumetric modulated arc therapy, algorithm, computer simulation, image processing, imaging phantom, magnetic and electromagnetic equipment, neoplasm, procedures, radiotherapy, radiotherapy planning system, Dosimetry

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-78350
https://doi.org/10.1088/2057-1976/adc73f

An enhanced deep learning approach for vascular wall fracture analysis

Autoren: Tragoudas, Alexandros Alloisio, Marta Elsayed, Elsayed S. et al.

Quelle: Archive of applied mechanics (1991). 94(9):2519-2532

Schlagwörter: Attention residual U-Net architecture, Deep learning, Experimental data, Fracture, Open-access source codes and data, Soft biological tissue, Vascular tissue

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-366637
https://doi.org/10.1007/s00419-024-02589-3

Deep learning assisted high‐resolution microscopy image processing for phase segmentation in functional composite materials

Autoren: Raghavendran, Ganesh Han, Bing Adekogbe, Fortune et al.

Quelle: Journal of Microscopy

Schlagwörter: 40 Engineering (for-2020), 4016 Materials Engineering (for-2020), Bioengineering (rcdc), deep learning, FFT, GUI tool, solid electrolyte interphase, transmission electron microscopy, U-Net, FFT, GUI tool, U‐Net, deep learning, solid electrolyte interphase, transmission electron microscopy, 0204 Condensed Matter Physics (for), 0601 Biochemistry and Cell Biology (for), 0912 Materials Engineering (for), Microscopy (science-metrix), 3101 Biochemistry and cell biology (for-2020), 3406 Physical chemistry (for-2020), 4016 Materials engineering (for-2020)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/58m4n3nv
https://escholarship.org/content/qt58m4n3nv/qt58m4n3nv.pdf

O-DAT: A novel framework integrating optimized dual attention for medical image segmentation

Autoren: Chen Guo Hongyuan Ren Haiying Qi et al.

Quelle: Alexandria Engineering Journal, Vol 128, Iss, Pp 878-890 (2025)

Schlagwörter: Medical image segmentation, TA1-2040, O-DAT, Engineering (General). Civil engineering (General), U-Net

Zugangs-URL: https://doaj.org/article/2363ff64908e4cd6a701a407cf5983c3

Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis

Autoren: Brian Kim Ziba Gandomkar Marnee J. McKay et al.

Quelle: Journal of Shoulder and Elbow Surgery. 34:2224-2238

Schlagwörter: Artificial intelligence, Basic Science Study, fatty infiltration, muscle volume, Computer Modeling Using AI/Machine Learning, convolutional neural network, rotator cuff, U-Net, Imaging, MRI

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39921123
https://research.vu.nl/en/publications/8a6cecd6-7f1d-411b-8333-f3e5a60f7f1e
https://doi.org/10.1016/j.jse.2024.12.033
https://hdl.handle.net/1871.1/8a6cecd6-7f1d-411b-8333-f3e5a60f7f1e

Application of Swin-UNet and pose estimation-based athlete motion quality detection device in IoT systems

Autoren: GuanLan Cai Guodong Zhang

Quelle: Alexandria Engineering Journal, Vol 128, Iss, Pp 100-116 (2025)

Schlagwörter: SwinUNetPose, Swin Transformer, IoT (Internet of Things), Human segmentation module, TA1-2040, Engineering (General). Civil engineering (General), U-Net, Pose estimation

Zugangs-URL: https://doaj.org/article/d8c702769b14462f91263dbd0162eb5b

Enhancing Facial Image Restoration Using CNN for Blur Severity Classification and U-Net for Deblurring

Autoren: Mauluddin, Muhammad Hidayat Supardi, Julian

Quelle: Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi; Vol. 10 No. 2 (2025): In Press: July, 2025; 110-120

Schlagwörter: Blur severity classification, Image deblurring, Convolutional Neural Network, U-Net

Dateibeschreibung: application/pdf

Zugangs-URL: https://ejournal.unitomo.ac.id/index.php/inform/article/view/9542

AI-Driven Battery-Free Wireless Sensing of Hazardous Liquid Spills via a Frequency-Selective Surface in a Monostatic Antenna Configuration

Autoren: Pau Casacuberta Fatemeh Niknahad Ali Maleki Gargari et al.

Quelle: Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona

Schlagwörter: Remote monitoring, Spill detection, Laboratory safety, Wireless sensing, Deep learning, Artificial intelligence (AI), U-Net, Frequency-selective surface (FSS)

Dateibeschreibung: application/pdf

Zugangs-URL: https://ddd.uab.cat/record/311285

A novel U-net model for brain tumor segmentation from MRI images

Autoren: Marwa Obayya Asma Alshuhail Khalid Mahmood et al.

Quelle: Alexandria Engineering Journal, Vol 126, Iss, Pp 220-230 (2025)

Schlagwörter: U-Net architecture, Dice Loss/IoU, LGG Segmentation Dataset, Deep learning, TA1-2040, Engineering (General). Civil engineering (General), Brain tumor segmentation

Zugangs-URL: https://doaj.org/article/acdbb2965cc94222a4e84d8b256d1818

3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS

Autoren: Sezin Barın Uçman Ergün Gür Emre Güraksın

Quelle: Volume: 9, Issue: 173-91
International Journal of 3D Printing Technologies and Digital Industry

Schlagwörter: Deep Learning, Image Processing, 3D Image Segmentation, Medical Image Analysis, U-Net, UNETR, Swin-Unet, Yazılım Mühendisliği (Diğer), Software Engineering (Other), 3D U-Net, SwinUNETR

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/ij3dptdi/issue/91345/1571288

Enhancing Interpretability in Retinal OCT Analysis Using Grad-CAM: A Study on the AROI Dataset

Autoren: Melinščak, Martina

Quelle: 2025 MIPRO 48th ICT and Electronics Convention. :1245-1250

Schlagwörter: retinal OCT, AROI dataset, interpretability, attention mechanism, U-Net, Grad-CAM

[PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies

Autoren: Collignon, Jordan Naeimi, Wesley Serio, Tricia R et al.

Quelle: Bulletin of Mathematical Biology. 87(1)

Schlagwörter: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Rare Diseases (rcdc), Neurodegenerative (rcdc), Genetics (rcdc), Transmissible Spongiform Encephalopathy (TSE) (rcdc), Neurosciences (rcdc), Brain Disorders (rcdc), Saccharomyces cerevisiae (mesh), Deep Learning (mesh), Prions (mesh), Phenotype (mesh), Mathematical Concepts (mesh), Neural Networks, Computer (mesh), Saccharomyces cerevisiae Proteins (mesh), Models, Biological (mesh), Computational Biology (mesh), Image Processing, Computer-Assisted (mesh), Peptide Termination Factors (mesh), U-Net, Image segmentation, Deep learning, Classification, Yeast, Prions, Saccharomyces cerevisiae (mesh), Saccharomyces cerevisiae Proteins (mesh), Prions (mesh), Peptide Termination Factors (mesh), Computational Biology (mesh), Phenotype (mesh), Models, Biological (mesh), Image Processing, Computer-Assisted (mesh), Mathematical Concepts (mesh), Deep Learning (mesh), Neural Networks, Computer (mesh), Classification, Deep learning, Image segmentation, Prions, U-Net, Yeast, Saccharomyces cerevisiae (mesh), Deep Learning (mesh), Prions (mesh), Phenotype (mesh), Mathematical Concepts (mesh), Neural Networks, Computer (mesh), Saccharomyces cerevisiae Proteins (mesh), Models, Biological (mesh), Computational Biology (mesh), Image Processing, Computer-Assisted (mesh), Peptide Termination Factors (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 49 Mathematical sciences (for-2020)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/3qf1613n
https://escholarship.org/content/qt3qf1613n/qt3qf1613n.pdf

Total-variation regularized U-Net for wildfire burned area mapping based on Sentinel-1 C-Band SAR backscattering data

Autoren: Zhang, Puzhao Ban, Yifang Nascetti, Andrea

Quelle: ISPRS journal of photogrammetry and remote sensing (Print). 203:301-313

Schlagwörter: Burned area, Deep learning, SAR, Segmentation, Sentinel-1, Total variation, U-Net, Wildfire

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-334942
https://doi.org/10.1016/j.isprsjprs.2023.07.024

ADVANCING FOREST LAND MONITORING IN ISTANBUL REGIONAL DIRECTORATE OF FORESTRY: INTEGRATING U-NET DEEP LEARNING

Autoren: Ergin Çağatay Çankaya Burhan Gencal Turan Sönmez

Quelle: Volume: 7, Issue: 126-44
ArtGRID-Journal of Architecture Engineering and Fine Arts

Schlagwörter: Artificial Intelligence (Other), Forest Management, Remote Sensing, Deep Learning, Istanbul, U-Net, Fotogrametri ve Uzaktan Algılama, Modelling and Simulation, Yapay Zeka (Diğer), Photogrammetry and Remote Sensing, Modelleme ve Simülasyon, Orman Amenajmanı, Uzaktan Algılama, Derin Öğrenme, İstanbul

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/artgrid/issue/93375/1709260

Sequential Hybrid Integration of U-Net and Fully Convolutional Networks with Mask R-CNN for Enhanced Building Boundary Segmentation from Satellite Imagery

Autoren: Rojgar Qarani Ismael Haval Abduljabbar Sadeq

Quelle: Zanco Journal of Pure and Applied Sciences, Vol 37, Iss 3 (2025)

Schlagwörter: Technology, Science, Deep Learning,U-Net, FCN, Mask R-CNN, Sequential Integration

Zugangs-URL: https://doaj.org/article/5de0e1d4824a462483a57afe6e92ada2

Deep learning based ResNet integrated U-Net approach for segmentation and classification of breast cancer images

Autoren: Betül Ersöz Ali Öter Seref Sagiroglu et al.

Quelle: Volume: 5, Issue: 112-22
Computers and Informatics

Schlagwörter: Artificial Intelligence (Other), Görüntü İşleme, Image Processing, Yapay Zeka (Diğer), Breast cancer, Deep learning, Histology, ResNet, U-Net

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/ci/issue/91787/1604037

Medical Image Segmentation Based on Hybrid Deep Learning Techniques

Autoren: Mohammed Abdulameer Aljanabi Noor Abd Alrazak Shnain

Quelle: Wasit Journal for Pure Sciences, Vol 4, Iss 2 (2025)

Schlagwörter: Image segmentation, Transformer encoder, Science, U-Net

Zugangs-URL: https://doaj.org/article/fa8d852c77254972900f3d748386e810

Application of YOLO and U-Net models for building material identification on segmented images

Autoren: Ruslan Voronkov Mykhailo Bezuglyi

Quelle: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol 15, Iss 2 (2025)

Schlagwörter: Environmental sciences, classification of building materials, YOLOv8, deep learning, Environmental engineering, GE1-350, TA170-171, neural networks, image segmentation, U-Net

Zugangs-URL: https://doaj.org/article/8cf836759cda4b3e9b35dc051f32a101