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dual-encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2018-2027页

作者： Tian, Chenxi Ma, Yuliang Cammon, Jared Fang, Feng Zhang, Yingchun Meng, Ming Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China Univ Houston Dept Biomed Engn Houston TX 77204 USA

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples;then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.

关键词： Electroencephalography Feature extraction Brain modeling Entropy Generative adversarial networks Emotion recognition Electrodes electroencephalogram dual-encoder variational autoencoder-generative adversarial network data augmentation

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MiFDeU: Multi-information fusion network based on dual-encoder for pelvic bones segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 147卷

作者： Ju, Fujiao Wu, Yichu Dong, Mingjie Zhao, Jingxin Beijing Univ Technol Coll Comp Sci Beijing 100124 Peoples R China Beijing Univ Technol Fac Mat & Mfg Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 4 Dept Orthopaed Beijing 100048 Peoples R China Natl Clin Res Ctr Orthoped Sports Med & Rehabil Beijing Peoples R China

The segmentation of bone fragments is crucial for preoperative planning and intraoperative navigation in reduction surgery. Recent advances in medical segmentation have predominantly focused on U-shaped frameworks that employ convolutional neural networks or transformer variants as the backbone. However, these frameworks, which rely on a single encoder, often struggle with integrating information from diverse features and processing irregular shapes in visual objects. Such limitations can reduce segmentation accuracy and impair generalization performance across different datasets. To address these issues, we introduce multi-information fusion network based on dual-encoder for pelvic bones segmentation. In order to capture global contextual information and local features simultaneously, our model takes alight resnet and a graph neural network with swin-pool module as dual-encoder for effectively representing the global and local topologies. We construct a high-low multi-dimensional paired attention in the bottleneck for fusing spatial and channel information from different dimensions. Instead of using the traditional dice loss in the unet-like architecture, our model employs both topological loss and boundary loss to enhance the goal optimization. In the experiments, our model achieves a substantially lower dice similarity coefficient and comparable 95 Hausdorff distance compared to other state-of-the-art. The experiments on across datasets verify the superiority and generalization of the proposed model.

关键词： Pelvic segmentation dual-encoder Graph neural network Transformer

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TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2025年 123卷 102527-102527页

作者： Yang, Xin Li, Dongxue Deng, Liwei Huang, Sijuan Wang, Jing Harbin Univ Sci & Technol Sch Automat Heilongjiang Prov Key Lab Complex Intelligent Syst Harbin 150080 Heilongjiang Peoples R China Guangdong Key Lab Nasopharyngeal Carcinoma Diag & Guangzhou 510060 Guangdong Peoples R China Collaborat Innovat Ctr Canc Med Guangzhou Peoples R China State Key Lab Oncol South China Guangzhou Peoples R China Sun Yat Sen Univ Ctr Canc Guangzhou Peoples R China Dept Radiat Oncol Guangzhou 510060 Guangdong Peoples R China South China Normal Univ Inst Brain Res & Rehabil Guangzhou 510631 Guangdong Peoples R China

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration methods, current approaches still face considerable challenges, such as insufficient capture of local details, difficulty in effectively modeling global contextual information, and limited robustness in handling complex deformations. These limitations hinder the precision of high-resolution registration, particularly when dealing with medical images with intricate structures. To address these issues, this paper presents a novel registration network (TCDE-Net), an unsupervised medical image registration method based on a dualencoder architecture. The dual encoders complement each other in feature extraction, enabling the model to effectively handle large-scale nonlinear deformations and capture intricate local details, thereby enhancing registration accuracy. Additionally, the detail-enhancement attention module aids in restoring fine-grained features, improving the network's capability to address complex deformations such as those at gray-white matter boundaries. Experimental results on the OASIS, IXI, and Hammers-n30r95 3D brain MR dataset demonstrate that this method outperforms commonly used registration techniques across multiple evaluation metrics, achieving superior performance and robustness. Our code is available at https://***/muzi dongxue/TCDE-Net.

关键词： Medical image registration dual-encoder 3D brain MRI Deep learning

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DE-Net: A dual-encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15914-15926页

作者： He, Zhipeng Liu, Jing Yang, Shuai Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing 210023 Peoples R China 31682 Troop Peoples Liberat Army Lanzhou 730000 Peoples R China

Semantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud data and the similarity in local geometric structures. Most current deep learning-based methods concentrate on designing local feature extraction modules while neglecting the significance of long-distance contextual information. Nevertheless, this contextual information is crucial for accurate object segmentation in large-scale scenes. To address this limitation, we propose a dual-encoder segmentation network called DE-Net. DE-Net effectively learns both the local and long-distance contextual information for each point to achieve accurate point segmentation. DE-Net consists of two main components: dual-encoder modules (DEMs) and gradient-aware pooling modules (GAPM). DEMs extract local geometry and long-distance contextual information for each point using positional and trigonometric encoding to distinguish complex geometric features. GAPMs aggregate global information effectively using dual-distance and xy gradient information. In addition, a prediction jitter module was introduced during training to address the issue of class imbalance and improve the network's prediction results. The experimental results on three public benchmarks demonstrate that DE-Net outperforms existing state-of-the-art methods, achieving mean intersection over union scores of 83.5%, 61.8%, and 63.9% on Toronto-3D, WHU-MLS, and S3DIS datasets, respectively.

关键词： Point cloud compression Feature extraction Three-dimensional displays Encoding Tail Transformers Jitter Deep learning dual-encoder semantic segmentation 3-D point cloud

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STRD-Net: A dual-encoder Semantic Segmentation Network for Urban Green Space Extraction

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yu, Mouzhe He, Liheng Shen, Zhehui Lv, Meng Nanjing Forestry Univ Coll Civil Engn Nanjing 210037 Peoples R China

Urban green spaces significantly influence the production and lifestyle of individuals. Deep learning methods using convolutional neural network (CNN) as the encoder have weak global feature extraction capabilities, often missing individual trees or small areas of low vegetation. Transformer series models have weak local feature extraction capabilities and perform poorly in distinguishing between small categories such as trees and low vegetation. Therefore, we propose a novel dual-encoder semantic segmentation model, swin transformer and resnet50 dual-encoder net (STRD-Net), which integrates a parallel swin transformer (ST) framework and a CNN framework, capable of accepting two different channel ratio images as input, enabling the model to capture both global and local features. In the ST encoder, a convolutional block attention module (CBAM) is added to the head to overcome the "salt-and-pepper" noise effect in extraction results. A new patch merging (NPM) module is added after each ST module to further enhance the local feature extraction capabilities of the ST encoder for urban green spaces. In the CNN encoder, an enhanced atrous spatial pyramid pooling (EASPP) module is added after the Resnet50 backbone extraction network to expand the receptive field of the CNN encoder and enhance the global feature extraction capabilities for urban green spaces. The model includes a single skip connection to ensure extraction accuracy while saving computational resources. Results on the Vaihingen and Potsdam datasets indicate that STRD-Net improves both local and global feature extraction capabilities in the extraction of urban green spaces. The code will be available at https://***/learn-zhezhe/STRD-Net.

关键词： Feature extraction Remote sensing Transformers Semantic segmentation Vegetation mapping Normalized difference vegetation index Convolutional neural networks dual-encoder land object extraction remote sensing images semantic segmentation urban green spaces

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D-Net: A dual-encoder network for image splicing forgery detection and localization

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PATTERN RECOGNITION 2024年 155卷

作者： Yang, Zonglin Liu, Bo Bi, Xiuli Xiao, Bin Li, Weisheng Wang, Guoyin Gao, Xinbo Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol 2 Chongwen Rd Chongqing 400065 Peoples R China

Many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these methods focus on validating local patches or local objects. We regard image splicing forgery detection as a binary classification task that distinguishes tampered and non-tampered regions by forensic fingerprints rather than semantic features. As the network goes deep, its representation ability becomes strong. However, the non-semantic forensic fingerprints can hardly be retained by normal CNNs in deep layers. We proposed a novel dual-encoder network (D-Net) for image splicing forgery detection to resolve these issues, employing an unfixed and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides structural information that assists the learning and detection of the forgeries. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Net for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Net and state-of-the-art methods, D-Net, without pre-training or training on a large number of forgery images, outperformed the other methods in pixel-level forgery detection. Moreover, it is stably robust to different anti-forensic attacks.

关键词： Image splicing forgery detection Tampered region localization Convolutional neural network Image fingerprints Global perspective dual-encoder

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CMS-net: Edge-aware multimodal MRI feature fusion for brain tumor segmentation

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IMAGE AND VISION COMPUTING 2025年 156卷

作者： Lv, Chunjie Li, Biyuan Wang, Xiuwei Cai, Pengfei Yang, Bo Jia, Xuefeng Yan, Jun Tianjin Univ Technol & Educ Sch Elect Engn Tianjin 300222 Peoples R China Tianjin Dev Zone Jingnuohanhai Data Technol Co Ltd Tianjin Peoples R China Tianjin Univ Sch Math Tianjin 300072 Peoples R China

With the growing application of artificial intelligence in medical image processing, multimodal MRI brain tumor segmentation has become crucial for clinical diagnosis and treatment. Accurate segmentation relies heavily on the effective utilization of multimodal information. However, most existing methods primarily focus on global and local deep semantic features, often overlooking critical aspects such as edge information and cross-channel correlations. To address these limitations while retaining the strengths of existing methods, we propose a novel brain tumor segmentation approach: an edge-aware feature fusion model based on a dual-encoder architecture. CMS-Net is a novel brain tumor segmentation model that integrates edge-aware fusion, cross-channel interaction, and spatial state feature extraction to fully leverage multimodal information for improved segmentation accuracy. The architecture comprises two main components: an encoder and a decoder. The encoder utilizes both convolutional downsampling and Smart Swin Transformer downsampling, with the latter employing Shifted Spatial Multi-Head Self-Attention (SSW-MSA) to capture global features and enhance long-range dependencies. The decoder reconstructs the image via the CMS-Block, which consists of three key modules: the Multi-Scale Deep Convolutional Cross-Channel Attention module (MDTA), the Spatial State Module (SSM), and the Boundary-Aware Feature Fusion module (SWA). CMS-Net's dual-encoder architecture allows for deep extraction of both local and global features, enhancing segmentation performance. MDTA generates attention maps through cross-channel covariance, while SSM models spatial context to improve the understanding of complex structures. The SWA module, combining SSW-MSA with pooling, subtraction, and convolution, facilitates feature fusion and edge extraction. Dice and Focal loss functions were introduced to optimize cross-channel and spatial feature extraction. Experimental results on the BraTS2018

关键词： Feature fusion Boundary awareness Spatial state Cross-channel feature extraction dual-encoder Brain tumor segmentation

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Enhancing Biomedical ReQA With Adversarial Hard In-Batch Negative Samples

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第5期20卷 2933-2944页

作者： Zhao, Bo Bai, Jun Li, Chen Zhang, Jianfei Rong, Wenge Ouyang, Yuanxin Xiong, Zhang Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

Question answering (QA) plays a vital role in biomedical natural language processing. Among question answering tasks, the retrieval question answering (ReQA) aims to directly retrieve the correct answer from candidates and has attracted much attention in the community for its efficiency. Recently, researchers have introduced ReQA into the biomedical domain as BioReQA. Typically BioReQA models rely on the dual-encoder to gain semantic representation and are trained following the settings of dense retrieval. However, they normally utilize easy in-batch negative samples in training process to avoid the extra forwarding cost and GPU memory required by encoding additional negative samples. However, hard negative samples have been proved more important with regard to the overall performance of BioReQA tasks. Therefore in this research, we focus on effectively constructing hard in-batch negative samples. Inspired by the classic linear assignment problem, we propose an Iterative Linear Assignment Grouping (ILAG) algorithm to construct hard in-batch negative samples. To further enhance performance for given hard batches in a low-resource scenario, we also employ adversarial training to augment the difficulty of batches. Extensive experiments have shown our proposed method's promising potential in the area of biomedical retrieval question answering.

关键词： Adversarial training biomedical question answering dual-encoder negative sample retrieval question answering

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Improving Biomedical ReQA With Consistent NLI-Transfer and Post-Whitening

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1864-1875页

作者： Bai, Jun Yin, Chuantao Wu, Zimeng Zhang, Jianfei Wang, Yanmeng Jia, Guanyi Rong, Wenge Xiong, Zhang Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sino French Engineer Sch Beijing 100191 Peoples R China Beihang Univ Shenyuan Honor Coll Beijing 100191 Peoples R China Ping Technol Beijing 100027 Peoples R China China Telecom Corp Ltd Beijing Res Inst Beijing 102209 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

Retrieval Question Answering (ReQA) is an essential mechanism of information sharing which aims to find the answer to a posed question from large-scale candidates. Currently, the most efficient solution is dual-encoder which has shown great potential in the general domain, while it still lacks research on biomedical ReQA. Obtaining a robust dual-encoder from biomedical datasets is challenging, as scarce annotated data are not enough to sufficiently train the model which results in over-fitting problems. In this work, we first build ReQA BioASQ datasets for retrieving answers to biomedical questions, which can facilitate the corresponding research. On that basis, we propose a framework to solve the over-fitting issue for robust biomedical answer retrieval. Under the proposed framework, we first pre-train dual-encoder on natural language inference (NLI) task before the training on biomedical ReQA, where we appropriately change the pre-training objective of NLI to improve the consistency between NLI and biomedical ReQA, which significantly improve the transferability. Moreover, to eliminate the feature redundancies of dual-encoder, consistent post-whitening is proposed to conduct decorrelation on the training and trained sentence embeddings. With extensive experiments, the proposed framework achieves promising results and exhibits significant improvement compared with various competitive methods.

关键词： Biomedical question answering retrieval question answering dual-encoder transfer learning whitening

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Knowledge-Grounded Attention-Based Neural Machine Translation Model

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APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2025年第1期2025卷

作者： Israr, Huma Khan, Safdar Abbas Tahir, Muhammad Ali Shahzad, Muhammad Khuram Ahmad, Muneer Zain, Jasni Mohamad Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Dept Comp Sci Islamabad Pakistan UNIV BIRMINGHAM Dept Comp Sci BIRMINGHAM England Univ Roehampton Dept Comp Sci London England Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IBD Shah Alam 40450 Malaysia

Neural machine translation (NMT) model processes sentences in isolation and ignores additional contextual or side information beyond sentences. The input text alone often provides limited knowledge to generate contextually correct and meaningful translation. Relying solely on the input text could yield translations that lack accuracy. Side information related to either source or target side is helpful in the context of NMT. In this study, we empirically show that training an NMT model with target-side additional information used as knowledge can significantly improve the translation quality. The acquired knowledge is leveraged in the encoder-/decoder-based model utilizing multiencoder framework. The additional encoder converts knowledge into dense semantic representation called attention. These attentions from the input sentence and additional knowledge are then combined into a unified attention. The decoder generates the translation by conditioning on both the input text and acquired knowledge. Evaluation of translation from Urdu to English with a low-resource setting yields promising results in terms of both perplexity reduction and improved BLEU scores. The proposed models in the respective group outperform in LSTM and GRU with attention mechanism by +3.1 and +2.9 BLEU score, respectively. Extensive analysis confirms our claim that the translations influenced by additional information may occasionally contain rare low-frequency words and faithful translation. Experimental results on a different language pair DE-EN demonstrate that our suggested method is more efficient and general.

关键词： BLEU decoder dual-encoder external information GRU LSTM machine translation perplexity transformer Urdu to English word embedding

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