Relevant Contextual Information Research Articles

Palliative Care in Turkey: Insights from experts through key informant interviews

BackgroundThe rising demand for palliative-care (PC) in Turkey, driven by cancer, has prompted increased attention since the national PC policy in 2010. Despite this, the healthcare system predominantly focuses on curative care, lacking PC integration. This is due to combination of administrative obstacles, fragmented coordination, education and training scarcity. Thus urgent strategies are required to address the growing PC gap. This qualitative study explores the perspectives of PC professionals and policymakers, providing valuable insights for national policy and program development. Material and methodThis study employed an exploratory approach using key informant interviews. Interviews were conducted using semi-structured questionnaire. It sought to collect relevant contextual information in order to achieve its aim. Thematic content analysis was employed to examine and interpret the data. ResultTwenty-one participants, comprising nurses, specialists, and oncologists, were interviewed. The findings are encompassed by eight themes. 1) Integrated Care, highlights the importance of cohesive collaboration among diverse healthcare providers, social care services, and primary care systems to ensure comprehensive and effective care. 2) Meeting social care needs underscores significance of addressing a wide spectrum of patient requirements, including psychosocial support. 3) PC education emphasizes necessity of equipping healthcare professionals with the requisite skills and knowledge through comprehensive training. 4) Legalizing do-notresuscitate orders draws attention to the critical discussion surrounding end-of-life decisions. 5) Empowering communities recognizes bridging knowledge gaps among patients and caregivers. 6) Decision-Making underscores the importance of informed and collaborative decision-making processes. 7) Cultural considerations urge the adoption of culturally sensitive approaches. 8) Ongoing challenges shed light on persistent issues such as provider attitudes, and administrative hurdles. ConclusionThis study highlights essential factors for establishing an integrated PC program for cancer patients in Turkey. The existing healthcare system in Turkey offers opportunities for advanced PC. Successful implementation demands strategic actions to facilitate meaningful transformation.

Multi-Scale Spatial Attention-Based Multi-Channel 2D Convolutional Network for Soil Property Prediction.

Visible near-infrared spectroscopy (VNIR) is extensively researched for obtaining soil property information due to its rapid, cost-effective, and environmentally friendly advantages. Despite its widespread application and significant achievements in soil property analysis, current soil prediction models continue to suffer from low accuracy. To address this issue, we propose a convolutional neural network model that can achieve high-precision soil property prediction by creating 2D multi-channel inputs and applying a multi-scale spatial attention mechanism. Initially, we explored two-dimensional multi-channel inputs for seven soil properties in the public LUCAS spectral dataset using the Gramian Angular Field (GAF) method and various preprocessing techniques. Subsequently, we developed a convolutional neural network model with a multi-scale spatial attention mechanism to improve the network's extraction of relevant spatial contextual information. Our proposed model showed superior performance in a statistical comparison with current state-of-the-art techniques. The RMSE (R²) values for various soil properties were as follows: organic carbon content (OC) of 19.083 (0.955), calcium carbonate content (CaCO3) of 24.901 (0.961), nitrogen content (N) of 0.969 (0.933), cation exchange capacity (CEC) of 6.52 (0.803), pH in H2O of 0.366 (0.927), clay content of 4.845 (0.86), and sand content of 12.069 (0.789). Our proposed model can effectively extract features from visible near-infrared spectroscopy data, contributing to the precise detection of soil properties.

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Background:Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model’s viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. Methods:We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. Results:The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. Conclusions:The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Deep Learning and Natural Language Processing Technology Based Display and Analysis of Modern Artwork

The modern artwork analysis display system, empowered by natural language processing (NLP) technology, revolutionizes the way audiences interact with and understand art. By integrating NLP algorithms, this system offers a dynamic and user-friendly platform for analyzing and displaying artwork. Utilizing NLP, visitors can engage in interactive conversations with the system, asking questions or making inquiries about the artwork on display. The system processes these inquiries, extracting relevant information from curated databases and scholarly sources to provide insightful and context-rich responses. Additionally, NLP algorithms can analyze textual descriptions, artist statements, and critical reviews to offer nuanced interpretations and historical context for each artwork. This paper presents the design and implementation of an innovative modern artwork analysis and display system, leveraging deep learning and natural language processing (NLP) technology, integrated with Multi-Feature Extraction Fuzzy Classification (MFEFC). The system offers a comprehensive platform for analyzing and presenting modern artworks, enhancing user engagement and understanding. Deep learning algorithms are employed to extract high-level features from visual artworks, allowing for automatic recognition of artistic styles, genres, and themes. Concurrently, NLP techniques process textual descriptions, artist biographies, and critical reviews to provide contextual information and interpretative insights. The integration of MFEFC enables precise classification of artworks based on multiple features extracted from both visual and textual sources, facilitating accurate analysis and categorization. Simulation of the NLP techniques demonstrated an average precision of 90% in extracting relevant contextual information from textual descriptions and artist biographies. Furthermore, MFEFC achieved a classification accuracy of 88% in categorizing artworks based on combined visual and textual features.

The importance of context: re-examining the ‘deployments’ of SWAT teams in Canada

ABSTRACT Based on an analysis of data released through Freedom of Information (FOI) requests, Canadian researchers have suggested that Special Weapons and Tactics (SWAT) teams are no longer exclusively deployed to resolve high-risk incidents but now frequently respond to routine calls that do not necessitate their involvement. Given concerns about these conclusions, we submitted the same FOI requests to the 14 police agencies examined by Roziere and Walby [2020. Special weapons and tactics teams in Canadian policing: legal, institutional, and economic dimensions. Policing and society, 30 (6), 704–719] and worked with the FOI analyst from each agency to ensure that the data were being interpreted correctly. Based on our re-analysis of the FOI-released data, we report on two problems with the conclusions reached by Roziere and Walby: the conflation of incidents where any SWAT officer responds to calls with full SWAT team deployments and the masking of potential risk factors in calls when relying on call type categories. Our findings illustrate the value of police agencies disclosing relevant contextual information to researchers when possible and they reinforce the necessity of collaborating with FOI analysts to better understand the data being released.

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.

Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

An in-context overview of legal translator competences, qualifications and other professional requirements set in the ISO 20771:2020 Legal translation – Requirements standard

Abstract This article provides an overview of legal translator competences, qualifications and other professional requirements set in the ISO 20771:2020 standard. References to existing frameworks, such as European Qualifications Framework (EQF) and some research, complementary ISO standards (notably ISO 17100:2015) and current industry best-practice offer relevant context and background information, and identify the gap that has been filled by this important standard. The author demonstrates that ISO 20771:2020 is a good example of a requirements standard developed for the translation industry that is up-to-date, reflects actual industry best-practice, and addresses a need for standardization of legal translator requirements for the purpose of certification. It is posited here that ISO 20771:2020 is a valuable resource for all stakeholders as it not only provides accurate information about standard market requirements for legal translation (and translation in general) but also contributes to translators’ professionalization by setting objective professional benchmarks. Additionally, ISO requirements standards are, by definition, pragmatic and operational, and therefore easier to validate through conformity assessment and certification than any other resources, and hence both the industry and academia can only benefit from increased ISO standards uptake. The overview of legal translator competences, qualifications and other professional requirements set in the ISO 20771:2020 standard reflects the ISO approach to setting translator requirements, and outlines some of the practical implications of translation industry standardization and certification in the hope that it might become the starting point for more discussions on this subject in the future.

“You are Telling the Story Yourself”: Defining and Developing Narrative Pictorial Warning Labels

ABSTRACTPictorial warning labels (PWLs) featuring narrative content are promising strategies for communicating health risks and motivating behavior change. The objectives of this study were to (1) identify what intrinsic features a PWL must have to be constructed as a narrative and (2) uncover in what ways narrative PWLs are perceived as being (in)effective. Seven online focus groups were conducted via Zoom with moderate and heavy drinkers (n = 30). Participants discussed a series of mockup PWLs designed to communicate the cancer risk of alcohol. The discussion revealed that a static image must include character, causality, and setting to help individuals construct the story. Specifically, the character should be discernible and believable so that individuals can infer risk information. Moreover, the connection between the image and text should imply a causal relationship between alcohol drinking and cancer risks. Lastly, there should be sufficient relevant background or context information. When discussing the label effectiveness, most participants thought narrative PWLs were more effective than graphic, non-narrative PWLs at informing consumers about the cancer risk of alcohol. Their reasoning included narrative PWLs (1) being easy to understand, (2) evoking curiosity and imagination, (3) eliciting sympathy for the character, (4) not causing aversion, and (5) increasing risk perceptions. This study contributes to the narrative persuasion research and offers practical implications for designing image-based narratives.

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine healthdata. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Earlier sexual debut as a risk factor for substance use among men who have sex with men (MSM) in Kazakhstan.

Limited research have examined predictors of illicit use of drugs and binge drinking among gay, bisexual, and other men who have sex with men (MSM) in Kazakhstan and Central Asia. This study examines earlier sexual debut as a risk factor for lifetime and recent substance use behaviors among MSM in Kazakhstan. We conducted a secondary analysis of self-reported data from a NIDA-funded HIV prevention trial including 902 adult cisgender MSM in Kazakhstan who completed structured screening interviews. Logistic regression models were used to estimate associations between earlier sexual debut (ages 16 and older as the reference group) and lifetime and recent substance use, with covariance adjustment for sociodemographic characteristics. The majority of MSM in our sample reported lifetime binge drinking behavior (73%) and illicit use of drugs (65%). Participants with an earlier sexual debut before 13 years old had significantly higher odds of lifetime binge drinking and any illicit use of drugs (aOR= 2.3, 95%CI: 1.2-4.5; aOR=3.0, 95%CI: 1.6-5.8). MSM who reported an earlier sexual debut between 13-15 years old had significantly higher odds of lifetime binge drinking and illicit use of any drugs (aOR=1.6, 95%CI: 1.1-2.3; aOR=1.6, 95%CI: 1.1-2.3); as well as recent binge drinking behavior (aOR=1.6, 95%CI: 1.2-2.3). Future research should examine pathways between earlier sexual experiences and substance use behaviors among sexually diverse populations. Earlier sexual experiences during childhood and adolescence may be relevant contextual information for interventions aimed at substance use risk prevention, treatment, and recovery among MSM populations.

Morphometric analysis of watershed

The analysis of drainage basins or watersheds based on morphometric parameters is highly significant for effective watershed planning. It provides valuable insights into the relationships between different characteristics within a given area. Although numerous technical papers have explored this field of study, there is currently no standardized classification system and clear interpretation for each parameter. Consequently, evaluating the significance of individual morphometric parameters can be confusing. This paper aims to address this issue by offering a comprehensive understanding of the meaning behind the values of various morphometric parameters, along with relevant contextual information. A critical review is provided for each classification, including the range of values and their implications. Emphasizing the importance of data quality, covering aspects such as data preparation, scale, and level of mapping detail is also done. This review paper is intended to provide a comprehensive explanation that will assist future researchers in their morphometric analysis studies.

AV PV-RCNN: Improving 3D Object Detection with Adaptive Deformation and VectorPool Aggregation

Three-dimensional object detection has attracted more and more attention from industry and academia due to its wide application in various fields such as autonomous driving and robotics. Currently, the refinement methods used by advanced two-stage detectors cannot fully adapt to different object scales, different point cloud densities, partial deformation and clutter, and excessive resource consumption. We propose a point cloud-based 3D object detection method that can adapt to different object scales and aggregate local features with less resources. The method first passes through an adaptive deformation module based on a 2D deformable convolutional network, which can adaptively collect instance-specific features from where the information content exists. Secondly, through a VectorPool aggregation module, this module can better aggregate local point features with less resource consumption. Finally, through a context fusion module, the key points can filter out relevant context information for the refinement stage. Our proposed detection method not only achieves better accuracy on the KITTI dataset, but also consumes less resources than the original detectors and has faster inference speed.

SaGCN: Semantic-Aware Graph Calibration Network for Temporal Sentence Grounding

Temporal sentence grounding is a challenging task that aims to localize the semantic corresponding segment from the untrimmed video according to the given query language description. Existing methods either utilize a cross-modal matching architecture following a scan-and-rank pipeline or directly predict the probabilities of being the target boundary for each frame based on the entire video content. However, such methods are weak when some of the critical semantic concepts in the query are actually relevant to multiple video segments or the desired video segment contains a query-irrelevant scene due to ignoring query semantic concepts and local and global cross-modal context. In this paper, we propose a novel semantic-aware graph calibration network (SaGCN) to address the issues mentioned above. Specifically, we first introduce a semantic-aware local relational graph module to capture the inherent relationships among the specific semantic concept relevant local contextual information for fine-grained cross-modal information interactions. Then, a semantic-aware global relational graph module is derived for global contextual information integration and achieving cross-modal alignment. Finally, an attention-based calibration module is designed for eliminating the irrelevant information maintained in the visual modality under the guidance of query description. Extensive experiments verify the effectiveness of our proposed SaGCN on two widely used datasets (Charades-STA and TACoS), in which we achieve significant and consistent improvement compared to the state-of-the-art approaches.

Time-domain adaptive attention network for single-channel speech separation

Recent years have witnessed a great progress in single-channel speech separation by applying self-attention based networks. Despite the excellent performance in mining relevant long-sequence contextual information, self-attention networks cannot perfectly focus on subtle details in speech signals, such as temporal or spectral continuity, spectral structure, and timbre. To tackle this problem, we proposed a time-domain adaptive attention network (TAANet) with local and global attention network. Channel and spatial attention are introduced in local attention networks to focus on subtle details of the speech signals (frame-level features). In the global attention networks, a self-attention mechanism is used to explore the global associations of the speech contexts (utterance-level features). Moreover, we model the speech signal serially using multiple local and global attention blocks. This cascade structure enables our model to focus on local and global features adaptively, compared with other speech separation feature extraction methods, further boosting the separation performance. Versus other end-to-end speech separation methods, extensive experiments on benchmark datasets demonstrate that our approach obtains a superior result. (20.7 dB of SI-SNRi and 20.9 dB of SDRi on WSJ0-2mix).

Aging-related effects on the controlled retrieval of semantic information.

The efficient use of knowledge requires semantic control processes to retrieve context-relevant information. So far, it is well-established that semantic knowledge, as measured with vocabulary tests, does not decline with aging. Yet, it is still unclear whether controlled retrieval-the context-driven retrieval of very specific aspects of semantic knowledge-deteriorates in aging, following the same fate as other forms of cognitive control. Here, we tackled this issue by comparing the performance of younger and older native Italian speakers during a semantic feature verification task. To manipulate the control demands, we parametrically varied the semantic salience of the target feature for the cue concept. As compared to their young counterparts, older adults showed worse performance (in terms of reaction times) as the salience of the target feature of the concept decreased. This result suggests that older people have relatively greater difficulties in regulating the activation within semantic representation when conditions pose high demands of controlled retrieval of semantic information. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

VABDC-Net: A framework for Visual-Caption Sentiment Recognition via spatio-depth visual attention and bi-directional caption processing

People are becoming accustomed to posting images and captions on social media platforms to express their opinions. Hence, Visual-Caption Sentiment Recognition (VCSR) has been a subject of growing attention recently. Thus, the correlation between visual and caption modalities is crucial for VCSR. However, most recent VCSR strategies concatenate features from the visual and caption modalities with the help of pre-trained deep learning models containing millions of trainable parameters without adding a dedicated attention module, ultimately leading to less desirable results. Motivated by this observation, we have proposed a novel model VABDC-Net, that integrates an attention module with the convolutional neural network to focus on the most relevant information from the visual modality and attentional tokenizer-based method to extract the most relevant contextual information from the caption modality. Demanding to this dire need, the following are the significant contributions of our experimentation: (1) an attentional tokenizer-based bi-directional caption branch to retrieve useful textual features from the captions, (2) an attentional visual branch to retrieve appropriate visual features, and (3) a cross-domain feature fusion to merge multi-modal features and predict sentiment. Thorough experimentation on two benchmark datasets, Twitter-15, with an accuracy of 83.80%, and Twitter-17, with an accuracy of 72.42%, indicates that our technique outperforms existing methods for VCSR.

Coherent mapping of position and head direction across auditory and visual cortex.

Neurons in primary visual cortex (V1) may not only signal current visual input but also relevant contextual information such as reward expectancy and the subject's spatial position. Such contextual representations need not be restricted to V1 but could participate in a coherent mapping throughout sensory cortices. Here, we show that spiking activity coherently represents a location-specific mapping across auditory cortex (AC) and lateral, secondary visual cortex (V2L) of freely moving rats engaged in a sensory detection task on a figure-8 maze. Single-unit activity of both areas showed extensive similarities in terms of spatial distribution, reliability, and position coding. Importantly, reconstructions of subject position based on spiking activity displayed decoding errors that were correlated between areas. Additionally, we found that head direction, but not locomotor speed or head angular velocity, was an important determinant of activity in AC and V2L. By contrast, variables related to the sensory task cues or to trial correctness and reward were not markedly encoded in AC and V2L. We conclude that sensory cortices participate in coherent, multimodal representations of the subject's sensory-specific location. These may provide a common reference frame for distributed cortical sensory and motor processes and may support crossmodal predictive processing.

Abstracts of Presentations to the Working Session on Improving Predictive Modeling of Mycotoxin Risk for Africa Held at the 3rd ASM2022 on 7 September 2022, in Stellenbosch, South Africa

In 2008, the African Postharvest Losses Information Systems project (APHLIS, accessed on 6 September 2022) developed an algorithm for estimating the scale of cereal postharvest losses (PHLs). The relevant scientific literature and contextual information was used to build profiles of the PHLs occurring along the value chains of nine cereal crops by country and province for 37 sub-Saharan African countries. The APHLIS provides estimates of PHL figures where direct measurements are not available. A pilot project was subsequently initiated to explore the possibility of supplementing these loss estimates with information on the aflatoxin risk. Using satellite data on drought and rainfall, a time series of agro-climatic aflatoxin risk warning maps for maize was developed covering the countries and provinces of sub-Saharan Africa. The agro-climatic risk warning maps for specific countries were shared with mycotoxin experts from those countries for review and comparison with their aflatoxin incidence datasets. The present Work Session was a unique opportunity for African food safety mycotoxins experts, as well as other international experts, to meet and deepen the discussion about prospects for using their experience and their data to validate and improve agro-climatic risk modeling approaches.

Curriculum Pre-training Heterogeneous Subgraph Transformer for Top- N Recommendation

To characterize complex and heterogeneous side information in recommender systems, the heterogeneous information network (HIN) has shown superior performance and attracted much research attention. In HIN, the rich entities, relations, and paths can be utilized to model the correlations of users and items; such a task setting is often called HIN-based recommendation . Although HIN provides a general approach to modeling rich side information, it lacks special consideration on the goal of the recommendation task. The aggregated context from the heterogeneous graph is likely to incorporate irrelevant information, and the learned representations are not specifically optimized according to the recommendation task. Therefore, there is a need to rethink how to leverage the useful information from HIN to accomplish the recommendation task. To address the above issues, we propose a Curriculum pre-training based HEterogeneous Subgraph Transformer (called CHEST ) with new data characterization , representation model, and learning algorithm . Specifically, we consider extracting useful information from HIN to compose the interaction-specific heterogeneous subgraph, containing highly relevant context information for recommendation. Then, we capture the rich semantics (e.g., graph structure and path semantics) within the subgraph via a heterogeneous subgraph Transformer, where we encode the subgraph into multi-slot sequence representations. Besides, we design a curriculum pre-training strategy to provide an elementary-to-advanced learning process. The elementary course focuses on capturing local context information within the subgraph, and the advanced course aims to learn global context information. In this way, we gradually capture useful semantic information from HIN for modeling user-item interactions. Extensive experiments conducted on four real-world datasets demonstrate the superiority of our proposed method over a number of competitive baselines, especially when only limited training data is available.

ARU-DGAN: A dual generative adversarial network based on attention residual U-Net for magneto-acousto-electrical image denoising.

Magneto-Acousto-Electrical Tomography (MAET) is a multi-physics coupling imaging modality that integrates the high resolution of ultrasound imaging with the high contrast of electrical impedance imaging. However, the quality of images obtained through this imaging technique can be easily compromised by environmental or experimental noise, thereby affecting the overall quality of the imaging results. Existing methods for magneto-acousto-electrical image denoising lack the capability to model local and global features of magneto-acousto-electrical images and are unable to extract the most relevant multi-scale contextual information to model the joint distribution of clean images and noise images. To address this issue, we propose a Dual Generative Adversarial Network based on Attention Residual U-Net (ARU-DGAN) for magneto-acousto-electrical image denoising. Specifically, our model approximates the joint distribution of magneto-acousto-electrical clean and noisy images from two perspectives: noise removal and noise generation. First, it transforms noisy images into clean ones through a denoiser; second, it converts clean images into noisy ones via a generator. Simultaneously, we design an Attention Residual U-Net (ARU) to serve as the backbone of the denoiser and generator in the Dual Generative Adversarial Network (DGAN). The ARU network adopts a residual mechanism and introduces a linear Self-Attention based on Cross-Normalization (CNorm-SA), which is proposed in this paper. This design allows the model to effectively extract the most relevant multi-scale contextual information while maintaining high resolution, thereby better modeling the local and global features of magneto-acousto-electrical images. Finally, extensive experiments on a real-world magneto-acousto-electrical image dataset constructed in this paper demonstrate significant improvements in preserving image details achieved by ARU-DGAN. Furthermore, compared to the state-of-the-art competitive methods, it exhibits a 0.3 dB increase in PSNR and an improvement of 0.47% in SSIM.