相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks
Manjit Kaur et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)
DDUNet: Dense Dense U-Net with Applications in Image Denoising
Fan Jia et al.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) (2021)
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
Kang Zhang et al.
CELL (2020)
Learning Medical Image Denoising with Deep Dynamic Residual Attention Network
S. M. A. Sharif et al.
MATHEMATICS (2020)
An Evolutionary DenseRes Deep Convolutional Neural Network for Medical Image Segmentation
Tahereh Hassanzadeh et al.
IEEE ACCESS (2020)
Adaptive Differential Evolution Based on Successful Experience Information
Lianzheng Cheng et al.
IEEE ACCESS (2020)
Medical image denoising using convolutional neural network: a residual learning approach
Worku Jifara et al.
JOURNAL OF SUPERCOMPUTING (2019)
Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising
Peng Liu et al.
MEDICAL IMAGE ANALYSIS (2019)
cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks
Yulong Wang et al.
SWARM AND EVOLUTIONARY COMPUTATION (2019)
An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation
Omid Tarkhaneh et al.
EXPERT SYSTEMS WITH APPLICATIONS (2019)
RIDNet: Recursive Information Distillation Network for Color Image Denoising
Shengkai Zhuo et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) (2019)
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
Xing Liu et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
Tahereh Hassanzadeh et al.
IEEE ACCESS (2019)
Designing neural networks through neuroevolution
Kenneth O. Stanley et al.
NATURE MACHINE INTELLIGENCE (2019)
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
Kai Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)
Metaheuristic design of feedforward neural networks: A review of two decades of research
Varun Kumar Ojha et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2017)
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Kai Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Mehdi S. M. Sajjadi et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
Densely Connected Convolutional Networks
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion
Shutao Li et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2012)
A comprehensive assessment of the structural similarity index
Richard Dosselmann et al.
SIGNAL IMAGE AND VIDEO PROCESSING (2011)
Focal liver lesion detection and characterization with diffusion-weighted MR imaging: Comparison with standard breath-hold T2-weighted imaging
Tejas Parikh et al.
RADIOLOGY (2008)
Image denoising by sparse 3-D transform-domain collaborative filtering
Kostadin Dabov et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2007)
Total Variation Wavelet-Based Medical Image Denoising
Yang Wang et al.
INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING (2006)
Statistical and heuristic image noise extraction (SHINE): a new method for processing poisson noise in scintigraphic images
P Hannequin et al.
PHYSICS IN MEDICINE AND BIOLOGY (2002)
Automated segmentation of multiple sclerosis lesions by model outlier detection
K Van Leemput et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2001)
分享论文
