Results 71 to 80 of about 914,658 (364)

Adversarial training and dilated convolutions for brain MRI segmentation

, 2017
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation.
JM Wolterink, K Kamnitsas, K Kamnitsas, M Havaei, N Srivastava, P Moeskops, P Moeskops, W Zhang, Y Ganin   +8 more
core   +1 more source

ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation [PDF]

IEEE Transactions on Medical Imaging
Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer ...
Zihan Li, Yuan Zheng, Dandan Shan, Shuzhou Yang, Qingde Li, Beizhan Wang, Yuan-ting Zhang, Qingqi Hong, Dinggang Shen   +8 more
semanticscholar   +1 more source

The IQ‐compete assay for measuring mitochondrial protein import efficiencies in living yeast cells

FEBS Letters, EarlyView.
The efficiency of mitochondrial protein import depends on the properties of the newly synthesized precursor proteins. The Import and de‐Quenching Competition (IQ‐compete) assay is a novel method to monitor the import efficiency of different proteins by fluorescence in living yeast cells.
Yasmin Hoffman, Annika Egeler, Saskia Rödl, Johannes M. Herrmann   +3 more
wiley   +1 more source

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

, 2019
Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision.
Ahmad, Sahar, Nie, Dong, Ren, Xuhua, Shen, Dinggang, Wang, Qian, Xiang, Lei, Yang, Fan, Zhang, Lichi   +7 more
core   +1 more source

SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation [PDF]

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
The adoption of Vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding ...
Shehan Perera, Pouyan Navard, Alper Yilmaz   +2 more
semanticscholar   +1 more source

ResUNet++: An Advanced Architecture for Medical Image Segmentation [PDF]

IEEE International Symposium on Multimedia, 2019
Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.
Debesh Jha, P. Smedsrud, M. Riegler, Dag Johansen, Thomas de Lange, P. Halvorsen, H. Johansen   +6 more
semanticscholar   +1 more source

By dawn or dusk—how circadian timing rewrites bacterial infection outcomes

FEBS Letters, EarlyView.
The circadian clock shapes immune function, yet its influence on infection outcomes is only beginning to be understood. This review highlights how circadian timing alters host responses to the bacterial pathogens Salmonella enterica, Listeria monocytogenes, and Streptococcus pneumoniae revealing that the effectiveness of immune defense depends not only
Devons Mo, Catherine S. Palmer, Jacqueline M. Kimmey   +2 more
wiley   +1 more source

A review of medical ocular image segmentation

Virtual Reality & Intelligent Hardware
Deep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015.
Lai WEI, Menghan HU
doaj   +1 more source

Self-Paced Learning With Diversity for Medical Image Segmentation by Using the Query-by-Committee and Dynamic Clustering Techniques

IEEE Access, 2021
Convolutional neural networks (CNNs), as a typical deep learning technique, have been widely used in image segmentation, but they often require a large amount of annotated data.
Xiaoying Pan, De Wei, Yizhe Zhao, Minjie Ma, Hao Wang   +4 more
doaj   +1 more source

Automatically Designing CNN Architectures for Medical Image Segmentation

, 2018
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources.
A Mortazi, KO Stanley, O Ronneberger
core   +1 more source