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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

PeerJ Computer science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS

arXiv

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arXiv 2025年

作者： Chen, Runhan Lin, Meijin Chen, Jianshu Lin, Liangjie Wang, Jiazheng Li, Xiaoqing Wang, Jianhua Huang, Xu Qian, Ling Liu, Shaoxing Long, Yuan Guo, Di Qu, Xiaobo Han, Haiwei National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China Department of Electronic Science Xiamen University Xiamen361102 China Clinical and Technical Support Philips Healthcare Beijing100016 China School of Economics and Management Xiamen University of Technology Xiamen361024 China Department of Radiology The First Hospital Affiliated to Xiamen University Xiamen361003 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

Given the need to elucidate the mechanisms underlying illnesses and their treatment, as well as the lack of harmonization of acquisition and post-processing protocols among different magnetic resonance system vendors, this work is to determine if metabolite concentrations obtained from different sessions, machine models and even different vendors of 3 T scanners can be highly reproducible and be pooled for diagnostic analysis, which is very valuable for the research of rare diseases. Participants underwent magnetic resonance imaging (MRI) scanning once on two separate days within one week (one session per day, each session including two proton magnetic resonance spectroscopy (1H-MRS) scans with no more than a 5-minute interval between scans (no off-bed activity)) on each machine. were analyzed for reliability of within- and between sessions using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), and for reproducibility of across the machines using correlation coefficient. As for within- and between- session, all CV values for a group of all the first or second scans of a session, or for a session were almost below 20%, and most of the ICCs for metabolites range from moderate (0.4≤ICC © 2025, CC BY.

关键词： Magnetic resonance imaging

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Traffic flow prediction using SPGAPSO-CKRVM model

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Revue d'Intelligence Artificielle 2020年第3期34卷 257-265页

作者： Lin, Hao Li, Leixiao Wang, Hui Wang, Yongsheng Ma, Zhiqiang College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China

Traffic flow prediction is popular research of ITS. Traffic flow prediction models based on machine learning have recently been widely applied. In this study, we use machine learning algorithms, heuristic algorithms, and parallelization technology to propose a traffic flow prediction model based on Relevance Vector Machine called Genetic Algorithm and Particle Swarm Optimization based on spark parallelization optimized Combined kernel RVM (SPGAPSO-CKRVM). First, combined kernel functions are constructed based on common kernel functions. Second, a parameter optimization algorithm is proposed to optimize the parameters of combined kernel functions by Genetic Algorithm and Particle Swarm Optimization. To reduce time consumed by the parameter optimization algorithm, we parallel the parameter optimization algorithm by Spark. Finally, the proposed model is verified with the real data of Whitemud Drive in Canada. The experimental results indicate that SPGAPSO-CKRVM has greater accuracy than other prediction models and parallelization technology reduce time consumed by the parameter optimization algorithm significantly. © 2020 Lavoisier. All rights reserved.

关键词： Heuristic algorithms

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Dual consistent constraint via disentangled consistency and complementarity for multi-view clustering

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Neurocomputing 2025年 641卷

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025

关键词： Contrastive Learning

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering

arXiv

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arXiv 2025年

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view’s complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025, CC BY.

关键词： Contrastive Learning

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Integrated Spatio-temporal Storyline Visualization with Low Crossover

Integrated Spatio-temporal Storyline Visualization with Low ...

引用

International Conference on Information Visualisation (IV)

作者： Xi He Ying Zhu College of Data Science and Software Engineering Wuzhou University Wuzhou China Creative Media Industries Institute Georgia State University Atlanta USA

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

A story is a chain of events. Each event has multiple dimensions, including time, location, characters, actions, and context. Storyline visualizations attempt to visually present the many dimensions of a story's events and their relationships. Integrating the temporal and spatial dimension in a single visualization view is often desirable but highly challenging. One of the main reasons is that spatial data is inherently 2D while temporal data is inherently 1D. We propose a storyline visualization technique that integrate both time and location information in a single view. We have developed a timeline-based visualization method that maintains regular ordering of temporal and spatial dimensions. We also developed a heuristic method to reduce excessive line crossovers for large data sets. Through case studies, we show that this visualization method provide a good balance in integrating temporal and spatial dimensions.

关键词： data analysis Two dimensional displays data visualization Spatial databases Synchronization

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An Indented Level-Based Tree Drawing Algorithm for Text Visualization

An Indented Level-Based Tree Drawing Algorithm for Text Visu...

引用

International Conference on Information Visualisation (IV)

作者： Xi He Ying Zhu College of Data Science and Software Engineering Wuzhou University Wuzhou China Creative Media Industries Institute Georgia State University Atlanta USA

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

Level-based tree drawing is a common algorithm that produces intuitive and clear presentations of hierarchically structured information. However, new applications often introduces new aesthetic requirements that call for new tree drawing methods. In this paper, we propose an indented level-based tree drawing algorithm for visualizing parse trees of English language. This algorithm displays a tree with an aspect ratio that fits the aspect ratio of the newer computer displays, while presenting the words in a way that is easy to read. We discuss the design of the algorithm and its application in text visualization for linguistic analysis and language learning. An efficient and practical implementation of the algorithm is also presented.

关键词： Visualization Computer displays Linguistics

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Quality Assurance for Automated Commit Message Generation

Quality Assurance for Automated Commit Message Generation

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Bei Wang Meng Yan Zhongxin Liu Ling Xu Xin Xia Xiaohong Zhang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Monash University Australia

ISBN: (纸本)9781728196305;9781728196312

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

关键词： Quality assurance Filtering Conferences Collaborative filtering Semantics Writing Filtering algorithms

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MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

MERITS: Medication Recommendation for Chronic Disease with I...

引用

IEEE International Conference on data Mining (ICDM)

作者： Shuai Zhang Jianxin Li Haoyi Zhou Qishan Zhu Shanghang Zhang Danding Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenyuan Honors College Beihang University China School of Software Beihang University China National University of Singapore >Singapore

ISBN: (纸本)9781665423991

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.

关键词： Correlation data visualization Ordinary differential equations data models Diabetes History Electronic medical records

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