Zobrazeno 1 - 10
of 51 334
pro vyhledávání: '"Medical image segmentation"'
Publikováno v:
Scientific Reports. 9/3/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Bao, Rina, Darzi, Erfan, He, Sheng, Hsiao, Chuan-Heng, Hussain, Mohammad Arafat, Li, Jingpeng, Bjornerud, Atle, Grant, Ellen, Ou, Yangming
Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or one-for-all solut
Externí odkaz:
http://arxiv.org/abs/2411.02745
Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personaliz
Externí odkaz:
http://arxiv.org/abs/2410.22223
In medical image segmentation tasks, the scarcity of labeled training data poses a significant challenge when training deep neural networks. When using U-Net-style architectures, it is common practice to address this problem by pretraining the encode
Externí odkaz:
http://arxiv.org/abs/2410.18677
Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention for its ability to utilize MM data and reduce reliance on labeled images. However, current methods face several challenges: (
Externí odkaz:
http://arxiv.org/abs/2410.17565
Autor:
Kareem, Daniya Najiha Abdul, Fiaz, Mustansar, Novershtern, Noa, Hanna, Jacob, Cholakkal, Hisham
Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encod
Externí odkaz:
http://arxiv.org/abs/2410.15360
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in comple
Externí odkaz:
http://arxiv.org/abs/2410.15036
Semi-supervised learning (SSL) for medical image segmentation is a challenging yet highly practical task, which reduces reliance on large-scale labeled dataset by leveraging unlabeled samples. Among SSL techniques, the weak-to-strong consistency fram
Externí odkaz:
http://arxiv.org/abs/2410.13486
Medical image segmentation is a pivotal step in diagnostic and therapeutic processes, relying on high-quality annotated data that is often challenging and costly to obtain. Semi-supervised learning offers a promising approach to enhance model perform
Externí odkaz:
http://arxiv.org/abs/2410.12419
Semi-supervised learning (SSL) techniques address the high labeling costs in 3D medical image segmentation, with the teacher-student model being a common approach. However, using an exponential moving average (EMA) in single-teacher models may cause
Externí odkaz:
http://arxiv.org/abs/2410.11509