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Attention-effective multiple instance learning on weakly stem cell colony segmentation

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arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale for Diagnosing Alzheimer’s Disease

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Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale fo...

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaolu Lin Pinya Lu Jie Pan Hongqin Yang Xuemei Ding Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Provincial University Engineering Research Center for Big Data Analysis and Application Fujian Normal University Fuzhou China Xiamen Jingyi Zhikang Technology Co. Ltd Xiamen China Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computing Engineering and Intelligent Systems Ulster University Derry-Londonderry NI UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases.

关键词： Neuroimaging Accuracy Attention mechanisms Three-dimensional displays Resonance Convolutional neural networks Alzheimer's disease

RELIANCE: A Reliable and Power Efficient LoRaWAN Optimization Approach for IoT systems

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RELIANCE: A Reliable and Power Efficient LoRaWAN Optimizatio...

IEEE World Forum on Internet of Things (WF-IoT)

作者： Ebrahim Darabi Azin Moradbeikiet Ahmad Keshavarz Sérgio Ivan Lopest Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo Portugal CiTin - Centro de Interface Tecnológico Industrial Inovarcos Arcos de Valdevez Portugal IT- Institutu de Telecomunicações Campus Universitário de Santiago Aveiro Portugal

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Long Range Wide Area Network (LoRaWAN) protocol has been proposed as an alternative for deploying secure communication networks in Industrial Internet-of-Things ecosystems. Typically, such systems run on batteries demanding low energy consumption, depend on reliable communications, and are deployed using a low-cost infrastructure based on gateways that connect to the LoRaWAN server in a simple star network topology. For example, indoor and outdoor assets localization and tracking may be critical in harsh environments, where industrial processes and operating machinery introduce noise and interference, leading the transmission channel to be less reliable. In this work, we present RELIANCE, a first approach to demonstrate the technical viability of using LoRaWAN for optimizing packet loss and energy efficiency when operating in dynamic and harsh environments. For the first time, RELIANCE takes advantage of image processing techniques to estimate the path loss of the environment. By correct path loss estimation, the proposed method determine the most optimal Transmission Power (TP) for the end nodes, based on their distance to the LoRaWAN gateways. Results have shown that, based on the proposed approach, an accurate path loss estimation method with an error of up to 2 dB is feasible for optimal TP prediction.

关键词： Object recognition Radio frequency Artificial neural networks Logic gates Security Reliability Indexes

Improving LoRaWAN Fingerprint-Based Localization by Detecting and Eliminating Noisy RSSI Measurements

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Improving LoRaWAN Fingerprint-Based Localization by Detectin...

IEEE World Forum on Internet of Things (WF-IoT)

作者： Asma Haghighat Zahra Sirous Azin Moradbeikie Ahmad Keshavarz Sérgio Ivan Lopes Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo Portugal CiTin-Centro de Interface Tecnológico Industrial Inovarcos Arcos de Valdevez Portugal IT-Instituto de Telecomunicações Campus Universitário de Santiago Aveiro Portugal

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Industry 5.0 (I5.0), is gaining more attention as a means of improving human life in the future. IoT plays a critical role in providing the necessary information and performing actions. Based on the wide range of industries, LPWAN and more specifically LoRaWAN is considered as a great progress to activate the IoT in the industry. However, to make the information provided by IoT meaningful, a localization method for end nodes is necessary. Fingerprint-based mapping is one of the developed methods for localization. Nonetheless, the frequent updates required for the fingerprint map pose a significant challenge. Additionally, the presence of noise in the fingerprint map, caused by environmental factors, significantly impacts localization accuracy. So, it is important to learn the environmental effects on the Received Signal Strength Indicator (RSSI) for performing a real differentiation between noisy and noiseless RSSI during the fingerprint map construction stage. By constructing an environment-aware fingerprint map and emphasizing the importance of noisy RSSI detection and elimination, this research contributes to enhancing the reliability and precision of LoRaWAN-based fingerprint-based localization systems by leveraging image processing techniques. For the evaluation step, a real implementation of proposed method in a car parking environment serves as testbed, where the influence of vehicular movements on RSSI measurements is prominent. The experimental results demonstrate that 83 percent accuracy in detecting noisy RSSI can be achieved by using the SVM classifier. This distinction between noisy and noiseless RSSI contributes to a significant improvement of 36 percent in location estimation accuracy.

关键词： Location awareness Temperature measurement Industries Image processing Noise Estimation Fingerprint recognition

Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem

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arXiv 2022年

作者： Variawa, Mohamed Z. Van Zyl, Terence L. Woolway, Matthew University of Johannesburg Academy of Computer Science and Software Engineering Johannesburg South Africa University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Johannesburg Faculty of Engineering and the Built Environment Johannesburg South Africa

Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical experiments or simulations. Cheap approximations of the objective function can reduce the computational requirements for solving these expensive optimisation problems. These cheap approximations may be machine learning or statistical models and are known as surrogate models. This paper introduces a simulation of a well-known batch processing problem in the literature. Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) are used to find the optimal schedule for the simulation. We then compare the quality of solutions obtained by the surrogate-assisted versions of the algorithms against the baseline algorithms. Surrogate-assistance is achieved through Probablistic Surrogate-Assisted Framework (PSAF). The results highlight the potential for improving baseline evolutionary algorithms through surrogates. For different time horizons, the solutions are evaluated with respect to several quality indicators. It is shown that the PSAF assisted GA (PSAF-GA) and PSAF-assisted DE (PSAF-DE) provided improvement in some time horizons. In others, they either maintained the solutions or showed some deterioration. The results also highlight the need to tune the hyper-parameters used by the surrogate-assisted framework, as the surrogate, in some instances, shows some deterioration over the baseline algorithm. © 2022, CC BY.

关键词： Machine learning

SGNet: Salient Geometric Network for Point Cloud Registration

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arXiv 2023年

作者： Wu, Qianliang Ding, Yaqing Luo, Lei Jiang, Haobo Gu, Shuo Zhou, Chuanwei Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

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arXiv 2023年

作者： Yan, Feng Jiang, Xiaoheng Lu, Yang Cui, Lisha Li, Shupan Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Electrical and Information Engineering Tianjin University Tianjin China the Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depthwise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% Fβw, 93.55% Sα, and 97.35% E) on SD-saliency-900 while running 272fps on a single gpu. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

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arXiv 2023年

作者： Jiang, Xiaoheng Guo, Kaiyi Lu, Yang Yan, Feng Liu, Hao Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Electrical and Information Engineering Tianjin University Tianjin China Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multilevel CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface defects