Results 11 to 20 of about 29,416,981 (354)

CE-Net: Context Encoder Network for 2D Medical Image Segmentation [PDF]

IEEE Transactions on Medical Imaging, 2019
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation ...
Jiang Liu, Zaiwang Gu, Kang Zhou
exaly   +2 more sources

TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers

Medical Image Analysis
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into ...
Qihang Yu, Yutong Xie, Yuyin Zhou
exaly   +2 more sources

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [PDF]

ECCV Workshops, 2021
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety of medical ...
Hu Cao, Yueyue Wang, Jieneng Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, Man-Ning Wang   +6 more
semanticscholar   +1 more source

UNETR: Transformers for 3D Medical Image Segmentation [PDF]

IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021
Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global
Ali Hatamizadeh, Yu-Cheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, B. Landman, Holger R. Roth, Daguang Xu   +7 more
semanticscholar   +1 more source

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [PDF]

Medical Image Analysis, 2023
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface.
Jun-De Wu, Rao Fu, Huihui Fang, Yuan-Pei Liu, Zhao-Wei Wang, Yanwu Xu, Yue-Ming Jin, T. Arbel   +7 more
semanticscholar   +1 more source

Medical imaging analysis with artificial neural networks [PDF]

, 2010
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection ...
Jiang, J., Ren, Jinchang, Trundle, P.
core   +4 more sources

EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks

Frontiers in Medicine, 2023
Background and purposeColorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide.
Liyu Shi, Xiaoyan Li, Weiming Hu, Haoyuan Chen, Jing Chen, Zizhen Fan, Minghe Gao, Yujie Jing, Guotao Lu, Deguo Ma, Zhiyu Ma, Qingtao Meng, Dechao Tang, Hongzan Sun, Marcin Grzegorzek, Marcin Grzegorzek, Shouliang Qi, Yueyang Teng, Chen Li   +18 more
doaj   +1 more source

UNet++: A Nested U-Net Architecture for Medical Image Segmentation [PDF]

DLMIA/ML-CDS@MICCAI, 2018
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of ...
Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang   +3 more
semanticscholar   +1 more source

A survey on deep learning in medical image analysis [PDF]

Medical Image Anal., 2017
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 ...
Geert Litjens, Thijs Kooi, B. Bejnordi, A. Setio, F. Ciompi, Mohsen Ghafoorian, J. van der Laak, B. van Ginneken, C. I. Sánchez   +8 more
semanticscholar   +1 more source

Analysis of carotid vulnerable plaque MRI high-risk features and clinical risk factors associated with concomitant acute cerebral infarction

BMC Cardiovascular Disorders, 2023
Background This study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI).
Yongxiang Tang, Jinping Zhang, Weizhou Liu, Wei Jin, Shijian Li, Zhen Qian, Xiaoquan Kong, Ran Zhang, Juanjuan HU, Baolin LI, Weiming Yuan, Yifan Zhang   +11 more
doaj   +1 more source