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SSDBN: A Single-Side Dual-Branch network with encoder-decoder for Building Extraction

REMOTE SENSING 2022年第3期14卷 768-768页

作者： Li, Yang Lu, Hui Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing Univ Informat Sci & Technol Minist Educ Sch Comp & Software Engn Res Ctr Digital Forens Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland

In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic segmentation using deep learning approaches, which can be further divided into two aspects. In this paper, we propose a single-side dual-branch network (SSDBN) based on an encoder-decoder structure, where an improved Res2Net model is used at the encoder stage to extract the basic feature information of prepared images while a dual-branch module is deployed at the decoder stage. An intermediate framework was designed using a new feature information fusion methods to capture more semantic information in a small area. The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details, respectively. All experiments were conducted using the Massachusetts Buildings Dataset and WHU Satellite Dataset I (global cities). The proposed model showed better performance than other recent approaches, achieving an F1-score of 87.69% and an IoU of 75.83% with a low network size volume (5.11 M), internal parameters (19.8 MB), and GFLOPs (22.54), on the Massachusetts Buildings Dataset.

关键词： building extraction dual-branch semantic segmentation encoder-decoder network

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MSANet: Mixed Spectral and Attention network for Robust 3D Human Pose Estimation

MSANet: Mixed Spectral and Attention Network for Robust 3D H...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Bing Wu, Suping Zhang, Xitie Shi, Liyuan Yang, Sheng Duan, Zhijian Xiong, Tuo Ningxia University School of Information Engineering Ningxia China

ISBN: (纸本)9798350368741

Despite significant advances in 3D human pose estimation from a single-view video, existing methods often struggle to produce reasonable human poses when the human is heavily occluded or blurred. To address this issue, we propose a Mixed Spectral and Attention network (MSANet) that stacks spectral and attention blocks alternately. The attention block captures visual cues before and after occlusions or blurs, while the spectral block perceives subtle localized occlusions or blurs for robust 3D human pose estimation. Specifically, our attention block captures the global information of intra-frame joints and enhances the coherent representation of inter-frame joints, the spectral block complements the intra- and inter-frame local occlusions information which is difficult to capture by the attention block. In addition, we improve the regression head (IRH) to narrow the grained gap between joint-level feature extraction and frame-level pose regression for smooth regression. With better temporal consistency and subtle localized occlusion awareness, our MSANet outperforms previous state-of-the-art methods on the commonly used benchmarks Human3.6M and MPI-INF-3DHP. Moreover, MSANet demonstrates broad real world applicability, realizing occlusions and blurs robust and accurate 3D pose estimation. The Code will be made public. © 2025 IEEE.

关键词： 3D Human Pose Estimation Attention Blurs encoder-decoder network Occlusions Spectral

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A model use context complementarity feature fusion learning for semi-supervised 3D medical image segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Chen, Lei Zhao, Yikai Yang, Dongxu Ma, Yunpeng Zhao, Bingjie Hou, Jieru Liu, Wenhao Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China

In 3D medical image segmentation, Semi-Supervised Learning (SSL) methods have shown strong potential with limited labeled data. However, most existing SSL models fail to effectively utilize correlations between data slices, leading to suboptimal segmentation boundaries. This paper presents a novel model for semi-supervised 3D medical image segmentation, namely, context complementary feature fusion learning (CCFFL). Specifically, we designed a novel encoder-decoder network and proposed context complementary and feature fusion learning modules. The context complementary module is embedded in the encoder-decoder structure. Rich spatial features are extracted in the encoding stage to avoid information loss, and high-resolution images are recovered in the decoding stage to improve the segmentation accuracy and the utilization efficiency of contextual information. Moreover, the feature fusion learning module evaluates, fuses, and enhances features extracted from CT and MRI slices and generates medical feature maps with global information. We assessed the CCFFL model in the left atrium and pancreas datasets and validated the feature mining capability of CCFFL in a series of ablation experiments. Experimental results show that our model outperforms most semi-supervised medical image segmentation models, with dice scores of 90.09 % and 77.76 % in the left atrium and pancreas, respectively.

关键词： Semi-supervised learning 3D medical image segmentation Data slices encoder-decoder network Context complementary module Feature fusion learning module

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Multimodal 3D medical image registration guided by shape encoder-decoder networks

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第2期15卷 269-276页

作者： Blendowski, Max Bouteldja, Nassim Heinrich, Mattias P. Univ Lubeck Inst Med Informat Ratzeburger Allee 160 D-23562 Lubeck Germany Rhein Westfal TH Aachen Inst Imaging & Comp Vis Templergraben 55 D-52056 Aachen Germany

Purpose Nonlinear multimodal image registration, for example, the fusion of computed tomography (CT) and magnetic resonance imaging (MRI), fundamentally depends on a definition of image similarity. Previous methods that derived modality-invariant representations focused on either global statistical grayscale relations or local structural similarity, both of which are prone to local optima. In contrast to most learning-based methods that rely on strong supervision of aligned multimodal image pairs, we aim to overcome this limitation for further practical use cases. Methods We propose a new concept that exploits anatomical shape information and requires only segmentation labels for both modalities individually. First, a shape-constrained encoder-decoder segmentation network without skip connections is jointly trained on labeled CT and MRI inputs. Second, an iterative energy-based minimization scheme is introduced that relies on the capability of the network to generate intermediate nonlinear shape representations. This further eases the multimodal alignment in the case of large deformations. Results Our novel approach robustly and accurately aligns 3D scans from the multimodal whole-heart segmentation dataset, outperforming classical unsupervised frameworks. Since both parts of our method rely on (stochastic) gradient optimization, it can be easily integrated in deep learning frameworks and executed on GPUs. Conclusions We present an integrated approach for weakly supervised multimodal image registration. Achieving promising results due to the exploration of intermediate shape features as registration guidance encourages further research in this direction.

关键词： Multimodal fusion Guided image registration encoder-decoder network Nonlinear shape interpolation

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Are all shortcuts in encoder-decoder networks beneficial for CT denoising?

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2023年第1期11卷 59-66页

作者： Chen, Junhua Zhang, Chong Wee, Leonard Dekker, Andre Bermejo, Inigo Maastricht Univ Grow Sch Oncol & Dev Biol Dept Radiat Oncol MAASTRO Med Ctr NL-6229 ET Maastricht Netherlands

Denoising of CT scans has attracted the attention of many researchers in the medical image analysis domain. encoder-decoder networks are deep learning neural networks that have become common for image denoising in recent years. Shortcuts between the encoder and decoder layers are crucial for some image-to-image translation tasks. However, are all shortcuts necessary for CT denoising? To answer this question, we set up two encoder-decoder networks representing two popular architectures and then progressively removed shortcuts from the networks from shallow to deep (forward removal) and from deep to shallow (backward removal). We used two unrelated datasets with different noise levels to test the denoising performance of these networks using two metrics, namely root mean square error and content loss. The results show that while more than half of the shortcuts are still indispensable for CT scan denoising, removing certain shortcuts leads to performance improvement for denoising. Both shallow and deep shortcuts might be removed, thus retaining sparse connections, especially when the noise level is high. Backward removal seems to have a better performance than forward removal, which means deep shortcuts have priority to be removed. Finally, we propose a hypothesis to explain this phenomenon and validate it in the experiments.

关键词： Deep learning encoder-decoder network medical image denoising shortcuts comparative analysis

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Deep convolutional encoder-decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2024年第4期6卷 408-424页

作者： Zhu, Huming Liu, Chendi Li, Qiuming Zhang, Lingyun Wang, Libing Li, Sifan Jiao, Licheng Hou, Biao Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China AVIC Xian Aeronaut Comp Tech Res Inst Xian Peoples R China China Star Network Applicat Co Ltd Chongqing Peoples R China

Due to the complexity of object information and optical conditions of high-resolution aerial imagery, it is difficult to obtain fine semantic segmentation performance. Although various deep neural network structures have been proposed to improve segmentation accuracy, there is still room for improving accuracy by making full use of multiscale features and integrating these single weak classifiers into a strong classifier. In this paper, we use a reduced SegNet network to realize the end-to-end classification of high-resolution aerial images. In addition, to use multiscale information, we present the R-SegUnet which combines the feature information of each convolution block in the reduced SegNet encoding network with the feature information of the corresponding convolution block in the decoding network. Furthermore, considering that the surface features in high-resolution aerial images are very complex, we investigate a 6to2_Net that converts the six-classification model into six binary-classification models for the recognition effect on small objects. Finally, we ensemble the above three different models to get the segmentation results. Experiment results on ISPRS Potsdam benchmark dataset show that our algorithm is state-of-the-art method. We also analyze the inference performance of our models on a variety of parallel computing devices.

关键词： Aerial imagery Semantic segmentation encoder-decoder network Ensemble ISPRS FCN

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Turning brain MRI into diagnostic PET: ¹⁵O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder-decoder networks

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MEDICAL IMAGE ANALYSIS 2024年 93卷 103072页

作者： Hussein, Ramy Shin, David Zhao, Moss Y. Guo, Jia Davidzon, Guido Steinberg, Gary Moseley, Michael Zaharchuk, Greg Stanford Univ Dept Radiol Radiol Sci Lab Stanford CA 94305 USA GE Healthcare Global MR Applicat & Workflow Menlo Pk CA 94025 USA Stanford Univ Stanford Cardiovasc Inst Stanford CA 94305 USA Univ Calif Riverside Dept Bioengn Riverside CA 92521 USA Stanford Univ Dept Radiol Div Nucl Med Stanford CA 94305 USA Stanford Univ Dept Neurosurg Stanford CA 94304 USA

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (O-15-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation. This study presents a convolutional encoder-decoder network with attention mechanisms to predict the gold-standard O-15-water PET CBF from multi-contrast MRI scans, thus eliminating the need for radioactive tracers. The model was trained and validated using 5-fold cross-validation in a group of 126 subjects consisting of healthy controls and cerebrovascular disease patients, all of whom underwent simultaneous O-15-water PET/MRI. The results demonstrate that the model can successfully synthesize high-quality PET CBF measurements (with an average SSIM of 0.924 and PSNR of 38.8 dB) and is more accurate compared to concurrent and previous PET synthesis methods. We also demonstrate the clinical significance of the proposed algorithm by evaluating the agreement for identifying the vascular territories with impaired CBF. Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.

关键词： PET multi-contrast MRI cerebrovascular disease Attention mechanisms encoder-decoder network

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Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks

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MEDICAL IMAGE ANALYSIS 2021年 69卷 101945-101945页

作者： Golbabaee, Mohammad Buonincontri, Guido Pirkl, Carolin M. Menzel, Marion, I Menze, Bjoern H. Davies, Mike Gomez, Pedro A. Univ Bath Comp Sci Dept Bath Avon England Imago7 Fdn Pisa Italy Tech Univ Munich Comp Sci Dept Munich Germany GE Healthcare Munich Germany Univ Edinburgh Sch Engn Edinburgh Midlothian Scotland

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Magnetic resonance fingerprinting Compressed sensing Convex model-based reconstruction Residual network encoder-decoder network

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network encoder-decoder network

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Video Foreground Extraction Using Multi-View Receptive Field and encoderdecoder DCNN for Traffic and Surveillance Applications

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第10期68卷 9478-9493页

作者： Akilan, Thangarajah Wu, Q. M. Jonathan Zhang, Wandong Lakehead Univ Dept Comp Sci Thunder Bay ON P7B 5E1 Canada Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

The automatic detection of foreground (FG) objects in videos is a demanding area of computer vision, with essential applications in video-based traffic analysis and surveillance. New solutions have attempted exploiting deep neural network (DNN) for this purpose. In DNN, learning agents, i.e., features for video FG object segmentation is nontrivial, unlike image segmentation. It is a temporally processed decision-making problem, where the agents involved are the spatial and temporal correlations of the FG objects and the background (BG) of the scene. To handle this and to overcome the conventional DL models poor delineation at the borders of FG regions due to fixed-view receptive filed-based learning, this work introduces a Multi-view Receptive Field encoder-decoder Convolutional Neural network called MvRF-CNN. The main contribution of the model is harnessing multiple views of convolutional (conv) kernels with residual feature fusions at early, mid and late stages in an encoder-decoder (EnDec) architecture. It enhances the ability of the model to learn condition-invariant agents resulting in highly delineated FG masks when compared to the existing approaches from heuristic- to DL-based techniques. The model is trained with sequence-specific labeled samples to predict scene-specific pixel-level labels of FG objects in near static scenes with a minute dynamism. The experimental study on 37 video sequences from traffic and surveillance scenarios that include complex environments, viz. dynamic background, camera jittery, intermittent object motion, scenes with cast shadows, night videos, and lousy weather proves the effectiveness of the model. The study covers two input configurations: a 3-channel (RGB) single frame and a 3-channel double-frame with a BG such that two consecutive grayscale frames stacked with a prior BG model. The ablation investigations are also conducted to show the importance of transfer learning (TL) and mid-fusion approaches for enhancing the segmen

关键词： Surveillance Image segmentation Cameras Object segmentation Convolutional neural networks Background subtraction encoder-decoder network foreground extraction transfer learning

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