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Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

computers, Materials & Continua 2021年第4期67卷 761-777页

作者： Afzan Adam Abdul Hadi Abd Rahman Nor Samsiah Sani Zaid Abdi Alkareem Alyessari Nur Jumaadzan Zaleha Mamat Basela Hasan Faculty of Information Science and Technology Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Faculty of Science and Technology School of Mathematical SciencesUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Department of Information Technology and Computing Faculty of Computer StudiesArab Open UniversityAmmanJordan

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling *** morphological pattern of an epithelial layer is greatly ***,the shape deformation model can normalise the distortions,but it needs a 2D *** theory,on the other hand,is not yet tested on digital pathology ***,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial *** boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight *** epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus ***,the textural and spatial features along the estimated layer are used for dysplastic tissue *** proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic *** result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth *** improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of ***,quantifying and classifying the morphological patterns for dysplasia can be *** textural and spatial features on the detected epithelial layer can capture the changes in tissue.

关键词： Digital pathology grading dysplasia tissue boundary effect

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SHARED ANCESTRY GRAPHS AND SYMBOLIC ARBOREAL MAPS

arXiv

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arXiv 2023年

作者： Huber, Katharina T. Moulton, Vincent Scholz, Guillaume E. School of Computing Sciences University of East Anglia Norwich United Kingdom Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany

A network N on a finite set X, |X| ≥ 2, is a connected directed acyclic graph with leaf set X in which every root in N has outdegree at least 2 and no vertex in N has indegree and outdegree equal to 1;N is arboreal if the underlying unrooted, undirected graph of N is a tree. Networks are of interest in evolutionary biology since they are used, for example, to represent the evolutionary history of a set X of species whose ancestors have exchanged genes in the past. For M some arbitrary set of symbols, (Equation presented) is a symbolic arboreal map if there exists some arboreal network N whose vertices with outdegree two or more are labelled by elements in M and so that (Equation presented) is equal to the label of the least common ancestor of x and y in N if this exists and else. Important examples of symbolic arboreal maps include the symbolic ultrametrics, which arise in areas such as game theory, phylogenetics and cograph theory. In this paper we show that a map (Equation presented) is a symbolic arboreal map if and only if d satisfies certain 3- and 4-point conditions and the graph with vertex set X and edge set consisting of those pairs (Equation presented) with d({x, y}) ≠ ☉ is Ptolemaic (i.e. its shortest path distance satisfies Ptolemy's inequality). To do this, we introduce and prove a key theorem concerning the shared ancestry graph for a network N on X, where this is the graph with vertex set X and edge set consisting of those (Equation presented) such that x and y share a common ancestor in N. In particular, we show that for any connected graph G with vertex set X and edge clique cover K in which there are no two distinct sets in K with one a subset of the other, there is some network with |K| roots and leaf set X whose shared ancestry graph is *** Codes 05C05, 05C20, 05C90 Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Generating Coupling Interfaces for Multiphysics Simulations with ExaStencils and waLBerla

Generating Coupling Interfaces for Multiphysics Simulations ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Richard Angersbach Sebastian Kuckuk Harald Köstler Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg National High Performance Computing Center Friedrich-Alexander-Universität Erlangen-Nürnberg

Domain-specific languages (DSLs) and code generation technology are adopted more and more by the high performance computing (HPC) community to tackle the performance, portability, and productivity requirements for HPC software. They share their inherent specialization in specific scientific domains with traditional HPC frameworks. In this regard, multiphysics simulations pose a strong challenge as they usually require components from multiple domains. A promising option for retaining these requirements is the combination of code generation with HPC frameworks through automatically created couplings. This work demonstrates the fusion of waLBerla with generated code from ExaStencils. waLBerla is a massively parallel C++ framework focused on computational fluid dynamics (CFD) simulations using the Lattice Boltzmann method (LBM). ExaStencils generates whole simulation codes using geometric multigrid solvers from specifications in its own DSL. We showcase the coupling of generated solvers with LBM simulations, including the mapping of data structures and augmentations to convert quantities units on-the-fly.

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Comparative Analysis of Unsupervised Pre-Training Models for LULC Classification With Limited Labelled Data

Comparative Analysis of Unsupervised Pre-Training Models for...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Deeksha Aggarwal Uttam Kumar Rikhil Gupta Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore (IIIT Bangalore) Bangalore India Department of Computer Science BITS Pilani Pilani India

Accurate classification of land use and land cover (LULC) categories from satellite imagery remains a pivotal task in remote sensing and geospatial analysis. The efficacy of classification models is often hindered by the scarcity of labeled samples and the degree of noise present in the labeled data, posing prevalent challenges in the real-world applications. This paper presents a study aimed at addressing these challenges by systematically comparing six combined generative pre-training and discriminative models with two standalone discriminative models in the context of LULC classification. Through meticulous empirical analysis, this study evaluates the performance of both generative and discriminative models when confronted with limited labeled data. The comparison not only highlighted the strengths and weaknesses of each model, but also provided insights into their adaptability and utility under constrained and noisy labeling resources, which are common scenarios in real-world LULC classification tasks. Additionally, the comparison offer insights into how generative models can leverage unlabeled data to augment their predictive capabilities, while also high-lighting the efficiency of traditional discriminative models when dealing with limited labeled data.

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IoT-Based Strawberry Disease Detection With Wall-Mounted Monitoring Cameras

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IEEE Internet of Things Journal 2024年第1期11卷 1439-1451页

作者： Lin, Yi-Bing Liu, Chun-You Chen, Wen-Liang Chang, Chia-Hui Ng, Fung-Ling Yang, Krista Hsung, Jerry Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan China Medical University College of Humanities and Sciences Taichung404 Taiwan National Cheng Kung University MiinWu School of Computing Tainan701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Yang Ming Chiao Tung University College of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Industrial Development Graduate Program College of Biological Science and Technology Hsinchu30010 Taiwan Proto Solution System Company Ltd. Hsinchu302052 Taiwan

This article proposes StrawberryTalk, an Internet of Things (IoT) platform for image-based strawberry disease detection. StrawberryTalk reuses the wall-mounted monitoring cameras without extra hardware cost. The contributions of StrawberryTalk are the utilization of IoT for automatic photo shoot and the wind detection mechanism to eliminate the obscure photos due to the wind effects. Also, the data preprocessing and the infection detection models are manipulated as IoT devices to simplify the implementation of the multicascade artificial intelligence (AI) models. We derive the relationship between the camera zoom factor and the distance between the camera and the strawberries for optimal disease detection. Accuracy of detection may be affected by obscure photos. In terms of eliminating obscure photos due to wind effect, we analytically derive the relationship between the wind alert delay and the number of obscure photos that must be retaken. For the greenhouses in the Bao Mountain, we only need to retake one photo. Based on the experiments, the mean average precision (mAP) of StrawberryTalk (to detect exact spots in a leaf) can be up to 92.37%, which is better than the previous approaches. To detect if a pot has infected leaves, the accuracy of StrawberryTalk can be up to 97.92% at the zoom factor 30×. In commercial operation, it is important to detect all infected strawberry pots. StrawberryTalk is able to detect all infected pots (i.e., recall is 100%) with a camera of zoom factor of 12× . The accuracy is 96.88%. © 2014 IEEE.

关键词： Cameras

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Open Tool-kit for AI-based Sleep Apnea Scoring

Open Tool-kit for AI-based Sleep Apnea Scoring

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Emerging Technology (INCET), International Conference for

作者： Karthika Ratheesh Krishna Rajeev Jyothika S Athira B Menon Rahul Krishnan Pathinarupothi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

Sleep is the most essential and fundamental to an individual’s well-being and vitality because it provides for the restoration and re-energizing of both the body and mind. Sleep apnea is a common sleep problem that affects millions of individuals worldwide. It is distinguished by breathing pauses or shallow breathing during sleeping, which can result in a variety of health problems such as heart disease, stroke, and diabetes [1]. Sleep apnea diagnosis and management can be difficult because the cost and time-consuming polysomnography (PSG) testing requires specialized equipment and trained personnel. To address these concerns, we created and validated an artificial intelligence (AI)-based sleep apnea scoring system that analyses electrocardiogram (ECG) signals to predict the severity of sleep apnea. The system analyses ECG signals using 1D-CNN to predict the Apnea-Hypopnea Index (AHI), a measure of the severity of sleep apnea. The tool is organized into three sections: data exploration, data visualization, and prediction, and it is aimed at accurately forecasting patients’ risk of sleep apnea. Our research demonstrates the potential of AI-based approaches for the diagnosis and management of sleep apnea, and we believe that our system can help improve patient outcomes and quality of life.

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Architecture and Implementation of a patient criticality aware Edge-Cloud offloading technique

Architecture and Implementation of a patient criticality awa...

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International Conference for Convergence of Technology (I2CT)

作者： Athira B Menon Krishna Rajeev Jyothika S Karthika Ratheesh Rahul Krishnan Pathinarupothi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

The Internet of Things (IoT)-based remote health monitoring is one of the most promising technological interventions that is emerging to address the unique challenges of affordability, accessibility, and availability in global health, which denote equitable access to healthcare, particularly in remote-rural-developing regions. IoT devices can monitor multiple vital signs, which are then used for AI-assisted decision-making systems, which in turn assist physicians in forecasting remotely. However, there are many difficulties, like bandwidth issues, data loss, and overburdened doctors due to the massive amount of data. Shifting data from cloud to edge improves performance, cost efficiency, privacy, reduces communication between distant servers and the edge, resulting in less processing delay. We developed a clinical response requirement based cloud-to-edge offloading technique. As a use case, we developed an edge AI application for acute hypotensive episode prediction and compared its performance both in the cloud as well as on the edge.

关键词： Performance evaluation Cloud computing Decision making Medical services Delays Internet of Things Servers

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Unsupervised 3D Point Cloud Completion via Multi-view Adversarial Learning

arXiv

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arXiv 2024年

作者： Wu, Lintai Cheng, Xianjing Xu, Yong Zeng, Huanqiang Hou, Junhui Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Guangdong Shenzhen518055 China School of Engineering Huaqiao University Quanzhou362021 China School of Information Science and Engineering Huaqiao University Xiamen361021 China

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The tasks of self-supervised and weakly-supervised point cloud completion involve reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current methods either rely on multiple views of partial observations for supervision or overlook the intrinsic geometric similarity that can be identified and utilized from the given partial point clouds. In this paper, we propose MAL-UPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures. Our MAL-UPC does not require any 3D complete supervision and only necessitates single-view partial observations in the training set. Specifically, we first introduce a Pattern Retrieval Network to retrieve similar position and curvature patterns between the partial input and the predicted shape, then leverage these similarities to densify and refine the reconstructed results. Additionally, we render the reconstructed complete shape into multi-view depth maps and design an adversarial learning module to learn the geometry of the target shape from category-specific single-view depth images of the partial point clouds in the training set. To achieve anisotropic rendering, we design a density-aware radius estimation algorithm to improve the quality of the rendered images. Our MAL-UPC outperforms current state-of-the-art self-supervised methods and even some unpaired approaches. We will make the source code publicly available at https://***/ltwu6/malspc. Copyright © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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ORA3D: Overlap Region Aware Multi-view 3D Object Detection 33

ORA3D: Overlap Region Aware Multi-view 3D Object Detection

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Roh, Wonseok Chang, Gyusam Moon, Seokha Nam, Giljoo Kim, Chanyoung Kim, Younghyun Kim, Sangpil Kim, Jinkyu Department of Artificial Intelligence Korea University Seoul Korea Republic of School of Computing KAIST Daejeon Korea Republic of Department of Computer Science and Engineering Korea University Seoul Korea Republic of Autonomous Driving Center Hyundai Motor Company R&D Division Seoul Korea Republic of

Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network. Moreover, objects in the overlap region are often largely occluded or suffer from deformation due to camera distortion, causing a domain shift. To mitigate this issue, we propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adversarial Overlap Region Discriminator. The former utilizes the traditional stereo disparity estimation method to obtain reliable disparity information from the overlap region. Given the disparity estimates as supervision, we propose regularizing the network to fully utilize the geometric potential of binocular images and improve the overall detection accuracy accordingly. Further, the latter module minimizes the representational gap between non-overlap and overlapping regions. We demonstrate the effectiveness of the proposed method with the nuScenes large-scale multi-view 3D object detection data. Our experiments show that our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Object detection

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Multimodal segmentation neural network to determine the cause of damage to grasslands 29

Multimodal segmentation neural network to determine the caus...

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29th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2021

作者： Johenneken, Maximilian Drak, Ahmad Herpers, Rainer Asteroth, Alexander Department of Computer Science Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Institute of Visual Computing Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Faculty of Computer Science University of New Brunswick Fredericton Canada Lassonde School of Engineering York University Toronto Canada Computer Science Department University College London London United Kingdom

ISBN: (纸本)9789532901092

Agricultural fields suffer from damage due to changing climate conditions and wildlife foraging. Damage incurred by wild boar is classified as the main contributing factor to grasslands damage. Such damage results in losses for farmers due to reduced yield potential and repair costs. The extent of wild boar damage is typically estimated manually by using ground based approaches (e.g. GPS measurements), which is a time consuming task with questionable accuracy. Building upon our previous work, we present an autonomous approach to detect and classify the cause of damage to grasslands (wild boar, mole etc.). The approach entails utilizing convolutional neural networks for semantic segmentation of grasslands. An RGB baseline was established, in addition to evaluating multimodal architectures that incorporate different surface model feature representations, leading to a joint representation of spectral and elevation information. Testing and experimentation was performed in real-world grasslands around Bonn, Germany. The results show that incorporating elevation features with late fusion enhances the overall performance of the network over the RGB baselines. © SoftCOM 2021. All rights reserved.

关键词： Semantic Segmentation

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