Suchergebnisse - Semi-supervised learning 3D medical image segmentation Class imbalance Data augmentation*

Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation

Autoren: Gu, Yuliang Liu, Yepeng Sun, Zhichao et al.

Weitere Verfasser: Gu, Yuliang Liu, Yepeng Sun, Zhichao et al.

Quelle: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :1966-1973

Schlagwörter: FOS: Computer and information sciences, Class imbalance, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Semi-supervised learning 3D medical image segmentation Class imbalance Data augmentation, Data augmentation, Computer Vision and Pattern Recognition (cs.CV), Semi-supervised learning, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), 3D medical image segmentation

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2410.14210
https://hal.science/hal-04739628v1

Deep Learning Network with Illuminant Augmentation for Diabetic Retinopathy Segmentation Using Comprehensive Anatomical Context Integration.

Autoren: Chankhachon, Sakon Kansomkeat, Supaporn Bhurayanontachai, Patama et al.

Quelle: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 21, p2762, 32p

Schlagwörter: DIABETIC retinopathy, IMAGE segmentation, COMPUTER-assisted image analysis (Medicine), FALSE positive error, DATA augmentation, DEEP learning, TRANSFORMER models, ANATOMICAL variation

Optimizing semi-supervised medical image segmentation with imbalanced filtering and nnU-Net enhancement.

Autoren: Duan, Yuanchuan Wang, Peng Huang, Yan et al.

Quelle: Visual Computer; Jun2025, Vol. 41 Issue 8, p5905-5917, 13p

Schlagwörter: COMPUTER-assisted image analysis (Medicine), IMAGE processing, ARTIFICIAL intelligence, DIAGNOSTIC imaging, SOURCE code

Source-Free Domain Adaptation for Medical Image Segmentation via Mutual Information Maximization and Prediction Bank.

Autoren: Wu, Hongzhen Zhou, Yue Li, Xiaoqiang

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 18, p3656, 20p

Schlagwörter: DATA privacy, IMAGE segmentation

Multi-task contrastive learning for semi-supervised medical image segmentation with multi-scale uncertainty estimation.

Autoren: Xing, Chengcheng Dong, Haoji Xi, Heran et al.

Quelle: Physics in Medicine & Biology; 9/21/2023, Vol. 68 Issue 18, p1-18, 18p

Schlagwörter: SUPERVISED learning, IMAGE segmentation, DIAGNOSTIC imaging, THERAPEUTICS, PREVENTIVE medicine

FR-nnUNet: a MRI image segmentation network based on the fuzzy regions recognition scheme and improved nnU-Net.

Autoren: Huang, Zizhen Wang, Lei Han, Yaolong et al.

Quelle: Signal, Image & Video Processing; Dec2024, Vol. 18 Issue 12, p9157-9168, 12p

Deep Transfer Learning from Constrained Source to Target Domains in Medical Image Segmentation.

Autoren: Krishnan, Chetana Schmidt, Emma Onuoha, Ezinwanne et al.

Quelle: Journal of Imaging Science & Technology; Nov/Dec2024, Vol. 68 Issue 6, p1-10, 10p

Schlagwörter: POLYCYSTIC kidney disease, MAGNETIC resonance imaging, CYSTIC kidney disease, COMPUTER-assisted image analysis (Medicine), IMAGE segmentation

HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding.

Autoren: Li, Feixiang Jiang, Ailian Li, Mengyang et al.

Quelle: Medical & Biological Engineering & Computing; Feb2024, Vol. 62 Issue 2, p405-421, 17p

Schlagwörter: DIAGNOSTIC imaging, FEATURE extraction, NOISE, ANNOTATIONS

ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet.

Autoren: Park, Minyoung Oh, Seungtaek Park, Junyoung et al.

Quelle: BMC Medical Imaging; 8/13/2025, Vol. 25 Issue 1, p1-23, 23p

Schlagwörter: DEEP learning, DATA augmentation, FEATURE extraction

MDFormer: a multi-scale dense dilated transformer model for 3D medical image segmentation.

Autoren: Li, Qiuxia Zhao, Haiyue Zhai, Ying et al.

Quelle: Scientific Reports; 10/30/2025, Vol. 15 Issue 1, p1-14, 14p

Schlagwörter: IMAGE segmentation, TRANSFORMER models, MATHEMATICAL optimization, MULTIPLE scale method, COMPUTER-assisted image analysis (Medicine)

Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging.

Autoren: Wang, Dongsheng Xv, Tiezhen Liu, Jiehui et al.

Quelle: Electronics (2079-9292); Aug2024, Vol. 13 Issue 15, p3086, 21p

Schlagwörter: THREE-dimensional imaging, SUPERVISED learning, DIAGNOSTIC imaging, DATABASES, PANCREAS

Enhancing Highway Scene Understanding: A Novel Data Augmentation Approach for Vehicle-Mounted LiDAR Point Cloud Segmentation.

Autoren: Zhou, Dalong Yi, Yuanyang Wang, Yu et al.

Quelle: Remote Sensing; Jul2025, Vol. 17 Issue 13, p2147, 22p

Schlagwörter: DATA augmentation, POINT cloud, SUPERVISED learning, SENSORY perception, INTELLIGENT transportation systems, IMAGE segmentation, TRANSPORTATION corridors

Unlabeled Insight, Labeled Boost: Contrastive Learning and Class-Adaptive Pseudo-Labeling for Semi-Supervised Medical Image Classification.

Autoren: Yang, Jing Chen, Mingliang Jia, Qinhao et al.

Quelle: Entropy; Oct2025, Vol. 27 Issue 10, p1015, 20p

Schlagwörter: COMPUTER-assisted image analysis (Medicine), DEEP learning, MACHINE learning, SUPERVISED learning, IMAGE processing

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

HHBSNet: a global channel–spatial attention and multi‐scale dilated convolution network for automatic melasma segmentation.

Autoren: Wang, Shange Xu, Lin Zhang, Linshuai et al.

Quelle: Frontiers in Physiology; 2025, p1-17, 17p

Schlagwörter: HYPERPIGMENTATION, IMAGE segmentation, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, SKIN discoloration, DEEP learning, COMPUTER-aided diagnosis

DAFSF: A Defect-Aware Fine Segmentation Framework Based on Hybrid Encoder and Adaptive Optimization for Image Analysis.

Autoren: Liu, Xiaoyi Zhu, Jianyu Zhu, Zhanyu et al.

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 21, p11351, 18p

Schlagwörter: IMAGE segmentation, IMAGE analysis, FEATURE extraction, MATHEMATICAL optimization, EDGE detection (Image processing), INDUSTRIAL applications

A Hybrid Framework for Red Blood Cell Labeling Using Elliptical Fitting, Autoencoding, and Data Augmentation.

Autoren: Angmanee, Bundasak Wanram, Surasak Thedsakhulwong, Amorn

Quelle: Journal of Imaging; Sep2025, Vol. 11 Issue 9, p309, 31p

Schlagwörter: THALASSEMIA, ANEMIA, CLASSIFICATION algorithms, DATA augmentation, FEATURE extraction, ERYTHROCYTES, HEMATOLOGY

Geografische Kategorien: THAILAND, SOUTHEAST Asia

Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning.

Autoren: Liu, Wei Mo, Jiaqing Zhong, Furu

Quelle: Applied Sciences (2076-3417); Feb2023, Vol. 13 Issue 4, p2109, 16p

Schlagwörter: SUPERVISED learning, MEDICAL coding, DIAGNOSTIC imaging

R-SWTNet: A Context-Aware U-Net-Based Framework for Segmenting Rural Roads and Alleys in China with the SQVillages Dataset.

Autoren: Wu, Jianing Yang, Junqi Xu, Xiaoyu et al.

Quelle: Land (2012); Oct2025, Vol. 14 Issue 10, p1930, 27p

Schlagwörter: RURAL roads, IMAGE segmentation, CONTEXT-aware computing, REMOTE sensing, INFRASTRUCTURE (Economics)

Geografische Kategorien: CHINA

Tackling class imbalance in computer vision: a contemporary review.

Autoren: Saini, Manisha Susan, Seba

Quelle: Artificial Intelligence Review; Oct2023 Suppl 1, Vol. 56, p1279-1335, 57p

Schlagwörter: DEEP learning, COMPUTER vision, MACHINE learning, TRANSFORMER models, GENERATIVE adversarial networks, ARTIFICIAL vision