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MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

computer Modeling in engineering & sciences 2021年第8期128卷 427-445页

作者： Juhong Tie Hui Peng Jiliu Zhou College of Computer Science Sichuan UniversityChengdu610065China School of Software Engineering Chengdu University of Information TechnologyChengdu610225China School of Computer Science Chengdu University of Information TechnologyChengdu610225China

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder *** used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

关键词： MRI brain tumor segmentation U-Net dense block residual block

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Periodic Attention Networks for Air Quality Index Prediction 23

Periodic Attention Networks for Air Quality Index Prediction

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Proceedings of the 2023 4th International Conference on Machine Learning and computer Application

作者： Jiaqing Wu Yinzhi Wu Cheng Chen School of Computer Science and Engineering & School of Software Guangxi Normal University China School of Civil and Architectural Engineering Yangtze Normal University China

ISBN: (纸本)9798400709449

Air quality remains a pressing concern in urban regions globally, influencing public health, environmental sustainability, and residents' overall well-being. However, predicting the subsequent day's air quality index (AQI) using its history data is very challenging due to environmental complexities. To address this issue, a novel model named Periodic Attention Networks (PAN) is proposed in this paper, which exploits the hierarchical attention mechanism to capture the periodicity of AQI changes. PAN does a good job of stratifying the weekly periodicity of Beijing's AQI with its monthly periodicity, which enables the model to be constructed with a distinction between important and unimportant contents. The experimental results on the regression prediction task of Beijing's AQI from 2019-2021 show that the applied model surpasses the previous methods to a large extent.

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Industry Upgrading and Public Opinion Prevention Using BERTopic 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Wang, Xinhua Yu, Xiaomei Jiang, Yunmeng Zheng, Xiangwei Li, Wei School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In the tourism industry, besides dining and attractions, which are hotspots for public opinion, the hotel industry is also a key area. The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In this context, it is crucial for hotels to address how to prevent such negative public opinion crises. In this paper, we use hotel review texts as an example to discuss how to use BERTopic and GRU networks to extract topics and analyze sentiment from guest reviews. This analysis helps understand guests’ focus and sentiment trends regarding hotels, enabling better grasp public opinion direction and user needs. Through topic extraction and sentiment analysis, we can extract crucial information from large scale hotel review texts. This enables monitor public opinion to help prevent large scale public opinion crises, allows for targeted improvements in the hotel industry, and supports decision-making for hotels and the tourism industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hotels

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HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

arXiv

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arXiv 2025年

作者： Lin, R.D. Weng, Pengcheng Wang, Yinqiao Ding, Han Han, Jinsong Wang, Fei School of Software Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China College of Computer Science and Technology Zhejiang University China

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

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作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Characterization, synthesis, and optimization of quantum circuits over multiple-control Z-rotation gates: A systematic study

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Physical Review A 2023年第2期108卷 022603-022603页

作者： Shihao Zhang Junda Wu Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China

We conduct a systematic study of quantum circuits composed of multiple-control Z-rotation (mczr) gates as primitives, since they are widely used components in quantum algorithms and also have attracted much experimental interest in recent years. We establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the mczr gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any mczr circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical investigation, the practical performance of our circuit synthesis and optimization techniques is further evaluated numerically on two typical examples in quantum computing, including diagonal Hermitian operators and quantum approximate optimization algorithm circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40% and 15.55% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.

关键词： Iterative methods

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Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

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computer Modeling in engineering & sciences 2023年第6期135卷 1965-1979页

作者： Ying Du Shuo Zhang Pu Cheng Rita Yi Man Li Xiao-Guang Yue School of Computer and Information Engineering Henan UniversityKaifeng475004China Henan Province Engineering Research Center of Spatial Information Processing Henan UniversityKaifeng475004China School of Software Henan UniversityKaifeng475004China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHong Kong999077China Department of Computer Science and Engineering European University CyprusNicosia1516Cyprus CIICESI-ESTG Politécnico do PortoFelgueiras4610-156Portugal

Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing *** solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling ***,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation ***,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from *** experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing *** results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.

关键词： Cloud computing reinforcement learning remote sensing task scheduling

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MMSite: A Multi-modal Framework for the Identification of Active Sites in Proteins 38

MMSite: A Multi-modal Framework for the Identification of Ac...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ouyang, Song Cai, Huiyu Luo, Yong Su, Kehua Zhang, Lefei Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China BioGeometry China Mila - Québec AI Institute Canada Université de Montréal Canada

The accurate identification of active sites in proteins is essential for the advancement of life sciences and pharmaceutical development, as these sites are of critical importance for enzyme activity and drug design. Recent advancements in protein language models (PLMs), trained on extensive datasets of amino acid sequences, have significantly improved our understanding of proteins. However, compared to the abundant protein sequence data, functional annotations, especially precise per-residue annotations, are scarce, which limits the performance of PLMs. On the other hand, textual descriptions of proteins, which could be annotated by human experts or a pretrained protein sequence-to-text model, provide meaningful context that could assist in the functional annotations, such as the localization of active sites. This motivates us to construct a ProTein-Attribute text Dataset (ProTAD), comprising over 570,000 pairs of protein sequences and multi-attribute textual descriptions. Based on this dataset, we propose MMSite, a multi-modal framework that improves the performance of PLMs to identify active sites by leveraging biomedical language models (BLMs). In particular, we incorporate manual prompting and design a MACross module to deal with the multi-attribute characteristics of textual descriptions. MMSite is a two-stage ("First Align, Then Fuse") framework: first aligns the textual modality with the sequential modality through soft-label alignment, and then identifies active sites via multi-modal fusion. Experimental results demonstrate that MMSite achieves state-of-the-art performance compared to existing protein representation learning methods. The dataset and code implementation are available at https://***/Gift-OYS/MMSite. © 2024 Neural information processing systems foundation. All rights reserved.

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