Results 271 to 280 of about 13,515,348 (328)

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic ...
Jiacheng Ruan, Jincheng Li, Suncheng Xiang   +2 more
semanticscholar   +1 more source

U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

AAAI Conference on Artificial Intelligence
U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are ...
Chenxin Li, Xinyu Liu, Wuyang Li, Cheng Wang, Hengyu Liu, Yixuan Yuan   +5 more
semanticscholar   +1 more source

SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention
The Transformer architecture has shown a remarkable ability in modeling global relationships. However, it poses a significant computational challenge when processing high-dimensional medical images. This hinders its development and widespread adoption in
Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu   +4 more
semanticscholar   +1 more source

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

arXiv.org
Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored,
Fan Bai, Yuxin Du, Tiejun Huang, Max Q.‐H. Meng, Bo Zhao   +4 more
semanticscholar   +1 more source

VoxelMorph: A Learning Framework for Deformable Medical Image Registration

IEEE Transactions on Medical Imaging, 2018
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich ...
Guha Balakrishnan, Amy Zhao, M. Sabuncu, J. Guttag, Adrian V. Dalca   +4 more
semanticscholar   +1 more source

Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency

Medical Image Anal., 2022
Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire.
Xiangde Luo, Guotai Wang, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, Dimitris N. Metaxas, Shaoting Zhang   +8 more
semanticscholar   +1 more source

Wavelets in medical imaging

AIP Conference Proceedings, 2012
The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact
Aslan, Zafar, Zahra, Noor E., Sevindir, Huliya A., Siddiqi, A. H.   +3 more
openaire   +3 more sources

nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
Fabian Isensee, Tassilo Wald, Constantin Ulrich, M. Baumgartner, Saikat Roy, Klaus H. Maier-Hein, P. Jaeger   +6 more
semanticscholar   +1 more source

Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions

Comput. Biol. Medicine
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision.
Yichi Zhang, Zhenrong Shen, Rushi Jiao
semanticscholar   +1 more source

Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

arXiv.org
In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks.
Ziyang Wang, Jian-Qing Zheng, Yichi Zhang, Ge Cui, Lei Li   +4 more
semanticscholar   +1 more source