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 von Gu, Yuliang, Liu, Yepeng, Sun, Zhichao, Zhu, Jinchi, Xu, Yongchao, Najman, Laurent

“… Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL …”
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When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation von Wang, Ziyang, Li, Tianze, Jian-Qing Zheng, Huang, Baoru

“… Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation tasks …”
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Consistency learning with dynamic weighting and class-agnostic regularization for semi-supervised medical image segmentation von Su, Jiawei, Luo, Zhiming, Lian, Sheng, Lin, Dazhen, Li, Shaozi

“… Recently, significant progress has been made in consistency regularization-based semi-supervised medical image segmentation …”
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Semi-Supervised Volumetric Medical Image Segmentation via Class Prototype Guided Distribution-Aligned Representation Learning von Kong, Xiangyu, Ren, Zeyu, Liu, Lu

“… We present SemiCRL, a novel framework for volumetric medical image segmentation that formulates an innovative contrastive learning methodology in a semi-supervised learning setting …”
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Semi-Supervised Learning for Medical Image Segmentation von Li, Ruizhe

“… Medical image segmentation is a fundamental step in many computer aided clinical applications, such as tumour detection and quantification, organ measurement and feature learning, etc …”
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Entropy‐guided contrastive learning for semi‐supervised medical image segmentation von Xie, Junsong, Wu, Qian, Zhu, Renju

“… ‐consuming and difficult to obtain. As a result, semi‐supervised learning (SSL) has gained attention as it has the potential to alleviate this challenge by using not only limited labelled data but also a large amount of unlabelled data …”
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Semi‐supervised medical image segmentation network based on mutual learning von Sun, Junmei, Wang, Tianyang, Wang, Meixi, Li, Xiumei, Xu, Yingying

“… Background Semi‐supervised learning provides an effective means to address the challenge of insufficient labeled data in medical image segmentation tasks …”
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Multidimensional perturbed consistency learning for semi‐supervised medical image segmentation von Yuan, Enze, Zhao, Bin, Qin, Xiao, Ding, Shuxue

“… ) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture …”
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Dual Diversity and Pseudo‐Label Correction Learning for Semi‐Supervised Medical Image Segmentation von Du, Guangxing, Wu, Rui, Xu, Jinming, Zeng, Xiang, Xiong, Shengwu

“… ABSTRACT Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large …”
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Dual‐Region Consistency Learning With Contrastive Refinement for Semi‐Supervised Medical Image Segmentation von Sun, Junmei, Wang, Meixi, Zhao, Jianxiang, Yang, Defu, Bai, Huang, Li, Xiumei

“… To address these issues, this paper proposes a novel semi‐supervised medical image segmentation framework named Dual …”
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FUSION: Uncertainty‐Guided Federated Semi‐Supervised Learning for Medical Image Segmentation von Raheem, Abdul, Yang, Zhen, Yu, Haiyang, Manan, Malik Abdul, Sabah, Fahad, Ahmed, Shahzad

“… Federated learning (FL) for medical image segmentation poses critical challenges, including non …”
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Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation von You, Chenyu, Zhao, Ruihan, Staib, Lawrence, Duncan, James S

“… Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation …”
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Combining contrastive learning and shape awareness for semi-supervised medical image segmentation von Chen, Yaqi, Chen, Faquan, Huang, Chenxi

“… Semi-supervised segmentation (SSL) techniques make extensive use of unlabeled data to address the issue of the high acquisition cost of medically labeled data …”
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Rectified Mixed-Label Learning for Semi-Supervised Medical Image Segmentation von An, Zeyu, Chen, Zichong

“… Semi-supervised medical image segmentation (SSMIS) has gained increasing attention due to its potential to alleviate the manual annotation burden …”
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EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation von He, Yuanpeng

“… Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is …”
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Learning Semi-Supervised Medical Image Segmentation from Spatial Registration von Liu, Qianying, Henderson, Paul, Gu, Xiao, Dai, Hang, Deligianni, Fani

“… Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data …”
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Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation von Ma, Yuxi, Wang, Jiacheng, Yang, Jing, Wang, Liansheng

“… Medical image segmentation is crucial in clinical diagnosis, helping physicians identify and analyze medical conditions …”
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Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation von Li, Lijian

“… Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance …”
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Consistency and adversarial semi-supervised learning for medical image segmentation von Tang, Yongqiang, Wang, Shilei, Qu, Yuxun, Cui, Zhihua, Zhang, Wensheng

“… To settle above issue, in this paper, a novel semi-supervised medical image segmentation method is proposed, in which the adversarial training mechanism and the collaborative consistency learning …”
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Mutual Evidential Deep Learning for Semi-supervised Medical Image Segmentation von He, Yuanpeng, Bi, Yali, Li, Lijian, Pun, Chi-Man, Jiao, Wenpin, Jin, Zhi

“… Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels …”
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