☆ 4.7 Article

Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2023)

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。

Article Computer Science, Artificial Intelligence

U2Fusion: A Unified Unsupervised Image Fusion Network

Han Xu et al.

Summary: This study proposes a novel unified and unsupervised end-to-end image fusion network, U2Fusion, which can solve different fusion problems. By training the model with adaptive information preservation, the network avoids the requirements of previous image fusion methods and has broad applicability. Moreover, a new dataset RoadScene is released for evaluation and comparison.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

SelfMatch: Robust semisupervised time-series classification with self-distillation

Huanlai Xing et al.

Summary: This study introduces a powerful semisupervised deep learning model SelfMatch, which combines supervised learning, unsupervised learning, and self-distillation techniques. Experimental results demonstrate that SelfMatch performs exceptionally well on 35 widely used UCR2018 datasets compared to various semisupervised and supervised algorithms.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-modal medical image fusion based on densely-connected high-resolution CNN and hybrid transformer

Quan Zhou et al.

Summary: This paper proposes an unsupervised multi-modal medical image fusion method based on deep learning and transformers. By using a densely-connected high-resolution network and a hybrid transformer, this method can effectively capture local information and long-range dependencies, resulting in better fused results.

NEURAL COMPUTING & APPLICATIONS (2022)

添加到收藏夹

Article Neurosciences

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer

Yun Jiang et al.

Summary: Brain tumor semantic segmentation is a critical task in medical image processing. This research proposes SwinBTS, a new approach that combines transformer, convolutional neural network, and encoder-decoder structure for 3D brain tumor segmentation. Experimental results demonstrate that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images.

BRAIN SCIENCES (2022)

添加到收藏夹

Article Automation & Control Systems

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

Jiayi Ma et al.

Summary: This study proposes a novel image fusion framework called SwinFusion, which combines cross-domain long-range learning and Swin Transformer. The framework integrates complementary information and achieves global interaction through attention-guided cross-domain modules. It also addresses multi-scene image fusion problems by preserving structure, detail, and intensity. Extensive experiments prove the superiority of SwinFusion compared to other state-of-the-art fusion algorithms. The implementation code and pre-trained weights are available at https://github.com/Linfeng-Tang/SwinFusion.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2022)

添加到收藏夹

Review Imaging Science & Photographic Technology

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

Andronicus A. Akinyelu et al.

Summary: This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, focusing on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. It highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques, while also discussing crucial issues and open challenges.

JOURNAL OF IMAGING (2022)

添加到收藏夹

Article Computer Science, Information Systems

MMI-Fuse: Multimodal Brain Image Fusion With Multiattention Module

Zhenghe Shi et al.

Summary: Medical imaging is crucial for diagnosing brain diseases. This paper proposes an end-to-end framework for multimodal brain image fusion, which improves the quality of fused images through an enhanced attention model and encoder-decoder structure.

IEEE ACCESS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

EMFusion: An unsupervised enhanced medical image fusion network

Han Xu et al.

Summary: This paper proposes an unsupervised enhanced medical image fusion network that preserves information through surface-level and deep-level constraints, while also enhancing the chrominance information of fusion results.

INFORMATION FUSION (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Multi-Source Information Exchange Encoding With PCNN for Medical Image Fusion

Rencan Nie et al.

Summary: This paper proposes a novel fusion framework for multimodal medical images based on PCNN and MIEE. The method exchanges and encodes information between different images, producing quantitative fusion contributions. Experimental results show that this approach outperforms other state-of-the-art fusion methods.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

添加到收藏夹

Article Computer Science, Information Systems

RTFN: A robust temporal feature network for time series classification

Zhiwen Xiao et al.

Summary: Time series data contains both local and global patterns, but existing feature networks focus on local features and neglect the relationships among them. Therefore, a novel RTFN method is proposed for feature extraction in time series, consisting of TFN and LSTMaN. Experimental results show that the RTFN-based structures achieve excellent performance on multiple datasets.

INFORMATION SCIENCES (2021)

添加到收藏夹

Review Engineering, Electrical & Electronic

Multimodal medical image fusion review: Theoretical background and recent advances

Haithem Hermessi et al.

Summary: The article discusses the development and application of medical image fusion methods, exploring the theoretical backgrounds and approaches of different fusion categories, summarizing the pros and cons of each category, and proposing directions for future research.

SIGNAL PROCESSING (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

SMFuse: Multi-Focus Image Fusion Via Self-Supervised Mask-Optimization

Jiayi Ma et al.

Summary: The SMFuse model utilizes self-supervised learning to generate a binary mask with Mask Generator. By using Guided-Block and guided filter to obtain initial binary mask and max-gradient loss to ensure all-in-focus fused image. Extensive experiments show the effectiveness and superiority of SMFuse compared to the current state-of-the-art.

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

IFCNN: A general image fusion framework based on convolutional neural network

Yu Zhang et al.

INFORMATION FUSION (2020)

添加到收藏夹

Article Chemistry, Analytical

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Kunpeng Wang et al.

SENSORS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multimodal medical image fusion algorithm in the era of big data

Wei Tan et al.

NEURAL COMPUTING & APPLICATIONS (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Laplacian Redecomposition for Multimodal Medical Image Fusion

Xiaoxiao Li et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

A Fast Image Fusion With Discrete Cosine Transform

Monan Wang et al.

IEEE SIGNAL PROCESSING LETTERS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Hui Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion

Jiayi Ma et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

Ming Yin et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis

Yu Liu et al.

IEEE SIGNAL PROCESSING LETTERS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

DenseFuse: A Fusion Approach to Infrared and Visible Images

Hui Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

MCFNet: Multi-Layer Concatenation Fusion Network for Medical Images Fusion

Xuecan Liang et al.

IEEE SENSORS JOURNAL (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

VoxelMorph: A Learning Framework for Deformable Medical Image Registration

Guha Balakrishnan et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

添加到收藏夹

Article Computer Science, Information Systems

A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain

Zhiqin Zhu et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Computer Science, Information Systems

A novel multi-modality image fusion method based on image decomposition and sparse representation

Zhiqin Zhu et al.

INFORMATION SCIENCES (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Robust Multi-Focus Image Fusion Using Multi-Task Sparse Representation and Spatial Context

Qiang Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An overview of multi-modal medical image fusion

Jiao Du et al.

NEUROCOMPUTING (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Union Laplacian pyramid with multiple features for medical image fusion

Jiao Du et al.

NEUROCOMPUTING (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Simultaneous image fusion and denoising with adaptive sparse representation

Yu Liu et al.

IET IMAGE PROCESSING (2015)

添加到收藏夹

Article Computer Science, Software Engineering

Region contrast and supervised locality-preserving projection-based saliency detection

Yanjiao Shi et al.

VISUAL COMPUTER (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A novel approach for multimodal medical image fusion

Zhaodong Liu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Medical image fusion: A survey of the state of the art

Alex Pappachen James et al.

INFORMATION FUSION (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Image Fusion with Guided Filtering

Shutao Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2013)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Deformable Medical Image Registration: A Survey

Aristeidis Sotiras et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2013)

添加到收藏夹

Article Computer Science, Artificial Intelligence

FSIM: A Feature Similarity Index for Image Quality Assessment

Lin Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2011)

添加到收藏夹

Article Engineering, Electrical & Electronic

Multifocus Image Fusion and Restoration With Sparse Representation

Bin Yang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2010)

添加到收藏夹

Article Mathematics, Applied

Sparse directional image representations using the discrete shearlet transform

Glenn Easley et al.

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2008)

添加到收藏夹

Editorial Material Engineering, Electrical & Electronic

Comments on 'Information measure for performance of image fusion'

M. Hossny et al.

ELECTRONICS LETTERS (2008)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Pixel- and region-based image fusion with complex wavelets

John J. Lewis et al.

INFORMATION FUSION (2007)

添加到收藏夹

Article Neurosciences

A general framework for the fusion of anatomical and functional medical images

V Barra et al.

NEUROIMAGE (2001)

添加到收藏夹

Article Engineering, Electrical & Electronic

Objective image fusion performance measure

CS Xydeas et al.

ELECTRONICS LETTERS (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.