Suchergebnisse - "((\"Deep encoder–decoder architecture\") OR (\"Deep encoder–design architecture\"))~"

Inception UNet architecture for breast tumor segmentation and detection using hybrid deep learning approach.

Autoren: Sharma, Ajay Mishra, Pramod Kumar

Quelle: Multimedia Tools & Applications; Jul2025, Vol. 84 Issue 24, p28225-28263, 39p

Schlagwörter: BREAST cancer, DEEP learning, TUMOR classification, TRANSFER of training, INTERDISCIPLINARY research, FEATURE extraction, ULTRASONIC imaging, EARLY diagnosis

Exploring the Use of Deep Learning Models for Image Compression in Embedded Systems: Encoder and Decoder Architectures.

Autoren: Potlapalli, Abhiram Khetavath, Seetharam

Quelle: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 15 Issue 1, p37-52, 16p

Schlagwörter: CONVOLUTIONAL neural networks, IMAGE processing, JPEG (Image coding standard), DEEP learning, ALGORITHMS, IMAGE compression, INSTRUCTIONAL systems

DUC-Net: A deep unified cross-modal architecture with cascaded feature learning for medical image segmentation.

Autoren: Memon, Yumna Zeng, Feng Khawaja, Faiza Riaz

Quelle: Cluster Computing; Dec2025, Vol. 28 Issue 16, p1-26, 26p

Developing Interpretable Deep Learning Model for Subtropical Forest Type Classification Using Beijing-2, Sentinel-1, and Time-Series NDVI Data of Sentinel-2.

Autoren: Chen, Shudan Wang, Xuefeng Shi, Mengmeng et al.

Quelle: Forests (19994907); Nov2025, Vol. 16 Issue 11, p1709, 21p

Schlagwörter: DEEP learning, REMOTE sensing, NORMALIZED difference vegetation index, FOREST site quality, ENVIRONMENTAL monitoring

An Efficient Vision Mamba–Transformer Hybrid Architecture for Abdominal Multi-Organ Image Segmentation.

Autoren: Lu, Fang Xu, Jingyu Sun, Qinxiu et al.

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6785, 18p

Schlagwörter: DEEP learning, TRANSFORMER models, IMAGE processing, DIAGNOSIS, COMPUTER-assisted image analysis (Medicine)

Enhancing hyperkinetic dysarthria speech recognition through deep cascade convolution and integrated attention mechanism.

Autoren: Mahamudul Hashan, Antor

Quelle: International Journal of Speech Technology; Sep2025, Vol. 28 Issue 3, p699-707, 9p

Schlagwörter: DYSARTHRIA, DEEP learning, TRANSCRIPTION (Linguistics), ERROR rates, SPEECH perception, MACHINE learning, HEALTH care industry

Firma/Körperschaft: DCC PLC

GOFENet: A Hybrid Transformer–CNN Network Integrating GEOBIA-Based Object Priors for Semantic Segmentation of Remote Sensing Images.

Autoren: He, Tao Chen, Jianyu Pan, Delu

Quelle: Remote Sensing; Aug2025, Vol. 7 Issue 15, p2652, 29p

Schlagwörter: REMOTE sensing, IMAGE segmentation, TRANSFORMER models, CONVOLUTIONAL neural networks, IMAGE analysis, DEEP learning, SPATIAL analysis (Statistics)

A Multi-Branch Deep Learning Network for Crop Classification Based on GF-2 Remote Sensing.

Autoren: Zhao, Lifang Zhang, Jiajin Yang, Hua et al.

Quelle: Remote Sensing; Aug2025, Vol. 17 Issue 16, p2852, 22p

Schlagwörter: DEEP learning, REMOTE sensing, AGRICULTURAL remote sensing, MULTISENSOR data fusion, LANDSCAPES, ARTIFICIAL neural networks, AGRICULTURAL technology

Geografische Kategorien: CHINA

Multimodal Artificial Intelligence in Medical Diagnostics.

Autoren: Jandoubi, Bassem Akhloufi, Moulay A.

Quelle: Information; Jul2025, Vol. 16 Issue 7, p591, 34p

Schlagwörter: CLINICAL decision making, ARTIFICIAL intelligence, MACHINE learning, ELECTRONIC health records, TRANSFORMER models, DEEP learning

DCM-Net: dual-encoder CNN-Mamba network with cross-branch fusion for robust medical image segmentation.

Autoren: Atabansi, Chukwuemeka Clinton Wang, Sheng Li, Hui et al.

Quelle: BMC Medical Imaging; 9/29/2025, Vol. 25 Issue 1, p1-24, 24p

Schlagwörter: IMAGE segmentation, TUMOR diagnosis, DEEP learning

MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation.

Autoren: Chen, Ruiyuan He, Saiqi Xie, Junjie et al.

Quelle: Scientific Reports; 2/11/2025, Vol. 15 Issue 1, p1-14, 14p

Schlagwörter: CONVOLUTIONAL neural networks, COMPUTER-assisted image analysis (Medicine), ARTIFICIAL intelligence, IMAGE processing, IMAGE segmentation, DEEP learning

DMA-Net: Dynamic Morphology-Aware Segmentation Network for Remote Sensing Images.

Autoren: Deng, Chao Liang, Haojian Qin, Xiao et al.

Quelle: Remote Sensing; Jul2025, Vol. 17 Issue 14, p2354, 30p

Schlagwörter: REMOTE-sensing images, IMAGE segmentation, FEATURE extraction, DEEP learning, ARTIFICIAL neural networks, TRANSFORMER models

Magnetic resonance image processing transformer for general accelerated image restoration.

Autoren: Shen, Guoyao Li, Mengyu Anderson, Stephan et al.

Quelle: Scientific Reports; 11/17/2025, Vol. 15 Issue 1, p1-15, 15p

Schlagwörter: MAGNETIC resonance imaging, TRANSFORMER models, COMPUTER performance, DEEP learning, SCALABILITY, FEATURE extraction, IMAGE processing

Chinese instrument music source separation with frequency-attentive multi-band neural networks.

Autoren: Zheng, Jiaxiang Cao, Moxi Zhang, Chongbin

Quelle: EURASIP Journal on Audio Speech & Music Processing; 10/27/2025, Vol. 2025 Issue 1, p1-24, 24p

Schlagwörter: MUSICAL instruments, DEEP learning, SPECTRAL sensitivity, ARTIFICIAL neural networks, AUDITORY scene analysis

A Lesion-Aware Patch Sampling Approach with EfficientNet3D-UNet for Robust Multiple Sclerosis Lesion Segmentation.

Autoren: Almaaz, Hind Dardouri, Samia

Quelle: Journal of Imaging; Oct2025, Vol. 11 Issue 10, p361, 19p

Schlagwörter: MULTIPLE sclerosis, DEEP learning, COMPUTER-assisted image analysis (Medicine), MAGNETIC resonance imaging, SAMPLING (Process), IMAGE segmentation

WDM-UNet: A Wavelet-Deformable Gated Fusion Network for Multi-Scale Retinal Vessel Segmentation.

Autoren: Li, Xinlong Zhou, Hang

Quelle: Sensors (14248220); Aug2025, Vol. 25 Issue 15, p4840, 24p

Schlagwörter: RETINAL blood vessels, IMAGE segmentation, DEEP learning, COMPUTER-assisted image analysis (Medicine), RETINAL imaging

SonarNet: Global Feature-Based Hybrid Attention Network for Side-Scan Sonar Image Segmentation.

Autoren: Lei, Juan Wang, Huigang Fan, Liming et al.

Quelle: Remote Sensing; Jul2025, Vol. 17 Issue 14, p2450, 20p

Schlagwörter: IMAGE segmentation, DEEP learning, UNDERWATER imaging systems, SONAR, ARTIFICIAL neural networks

Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.

Autoren: Silveira, Jaysel Theresa S., Girisha Kundapur, Poornima Panduranga

Quelle: Scientific Reports; 11/10/2025, Vol. 15 Issue 1, p1-19, 19p

Schlagwörter: IMAGE segmentation, MAGNETIC resonance imaging, FEATURE extraction, DEEP learning, COMPUTER-assisted image analysis (Medicine), SEGMENTATION (Biology), LUMBAR vertebrae

Generative adversarial networks for handwriting image generation: a review: Generative adversarial networks for handwriting image generation: a review: R. Elanwar, M. Betke.

Autoren: Elanwar, Randa Betke, Margrit

Quelle: Visual Computer; Mar2025, Vol. 41 Issue 4, p2299-2322, 24p

Schlagwörter: GENERATIVE adversarial networks, ARTIFICIAL intelligence, COGNITIVE psychology, IMAGE processing, HANDWRITING, DEEP learning

A Review of Vision-Based Multi-Task Perception Research Methods for Autonomous Vehicles.

Autoren: Wang, Hai Li, Jiayi Dong, Haoran

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2611, 36p

Schlagwörter: OBJECT recognition (Computer vision), DEEP learning, RESEARCH personnel, RESEARCH methodology, CLASSIFICATION, INTELLIGENT transportation systems