Results 261 to 270 of about 169,019 (308)

Risk of Retinopathy Associated with Long‐Term Use of Hydroxychloroquine in Patients with Rheumatic Diseases: A Systematic Review and Meta‐Analysis

Arthritis Care &Research, EarlyView.
Objective We aimed to estimate the prevalence and cumulative incidence of hydroxychloroquine retinopathy (HCQ‐R) and its risk factors among patients receiving long‐term HCQ with rheumatic diseases through a systematic review and meta‐analysis of observational studies that used spectral‐domain optical coherence tomography (SD‐OCT) for screening ...
Narsis Daftarian, Chloe Yue, Steve D Levasseur, Hui Xie, J. Antonio Avina‐Zubieta   +4 more
wiley   +1 more source

IAP-TransUNet: integration of the attention mechanism and pyramid pooling for medical image segmentation. [PDF]

Front Neurorobot
Shi Y, Li F, Zhao S, Yu H, Chen X, Liu Q.   +5 more
europepmc   +1 more source

MedPTQ: a practical pipeline for real post-training quantization in 3D medical image segmentation. [PDF]

J Med Imaging (Bellingham)
Qu C, Zhao R, Yu Y, Liu B, Yao T, Zhu J, Landman BA, Tang Y, Huo Y.   +8 more
europepmc   +1 more source

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent. [PDF]

Virtual Real
Spiegler P, Harirpoush A, Xiao Y.
europepmc   +1 more source

Volumetric Medical Image Segmentation Through Dual Self-Distillation in U-Shaped Networks. [PDF]

IEEE Trans Biomed Eng
Banerjee S, Summerfield N, Dong M, Glide-Hurst C.   +3 more
europepmc   +1 more source

VT-MFLV: Vision-Text Multimodal Feature Learning V Network for Medical Image Segmentation. [PDF]

J Imaging
Wang W, Li J, Ye Z, Cai Y, Wang Z, Zhang R.   +5 more
europepmc   +1 more source

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Segmentation of medical images

Image and Vision Computing, 1993
Abstract Segmentation and labelling remains the weakest step in many medical vision applications. This paper illustrates an approach based on generic modules which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters.
Rudi Deklerck, Jan Cornelis 0001, Michel Bister   +2 more
openaire   +2 more sources

DRINet for Medical Image Segmentation

IEEE Transactions on Medical Imaging, 2018
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution
Liang Chen 0018, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert   +5 more
openaire   +3 more sources

Medical image segmentation with MARA

1990 IJCNN International Joint Conference on Neural Networks, 1990
The multilayer adaptive resonance architecture (MARA) is a highly stable and plastic self-organizing neural network which is capable of recognizing, reconstructing, and segmenting the traces of previously learned binary patterns. The recognition and reconstruction properties of the network are invariant with respect to distortion, noise, translation ...
Jagath C. Rajapakse, Raj Acharya
openaire   +1 more source