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Extracting Eye Models from MRI Scans Using U-Net-Based Deep Learning Framework

Journal of computer and Communications 2024年第11期12卷 95-107页

作者： Waqar Ahmad Tahir Opeyemi Sheu Alamu Dip Sarker Md Tafhimul Haque Sadi Abdullah Al Hasib Tonmoy Kumar Sarker Md Rakibul Islam Dipankar Roy Dip Ashish Sharma Syed Wajeeh Abbas Rizvi Department of Internal Medicine/Casualty Khalifa Gull Nawaz Teaching Hospital Bannu Pakistan Department of Statistics Federal College of Animal Health and Production Technology Ibadan Nigeria Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Internal Medicine Arogya Hospital Pvt. Ltd. Baglung Nepal Department of Data Science National University of Computer & Emerging Sciences (FAST City Campus) Karachi Pakistan

.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.

关键词： Eye Model Extraction U-Net Deep Learning MRI Segmentation Ellipsoid and Non-Linear Models Mean Squared Error(MSE)

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A Regularized Hypergraph Recommendation Algorithm with Attention Mechanism 7

A Regularized Hypergraph Recommendation Algorithm with Atten...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Zhu, Tingting Chen, Jianrui Wang, Zhihui He, Meixia The Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China Department of Computer Science Shaanxi Normal University Xi'an710199 China

ISBN: (纸本)9781665441490

Collaborative filtering is one of the most commonly used recommendation technologies in recommender systems. However, it has the problems of sparse score data and low recommendation accuracy. To address these problems, we propose a regularized hypergraph recommendation algorithm with attention mechanism. We can fully mine the high-order relationships in the hyperedges without loss of information. In addition, we apply users' attributes information to calculate the attraction of an item to the attributes, and then get the similarity between items. In order to calculate the similarity more accurately, the relations of items to attributes are divided into two cases: likes and dislikes. Moreover, according to the different attention of users to different items, we introduce the attention mechanism. Finally, we build and optimize the regularization function to obtain the predictive scores and recommendation lists. Experimental results on Movielens-100K and Movielens-1M demonstrate the effectiveness of our proposed algorithm. © 2021 IEEE.

关键词： Collaborative filtering

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Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation

arXiv

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arXiv 2023年

作者： Bungay, James Emokpae, Osasenaga Relton, Samuel D. Alty, Jane Williams, Stefan Fang, Hui Wong, David C. The Department of Computer Science University of Manchester United Kingdom The Leeds Institute of Health Sciences University of Leeds United Kingdom The University of Tasmania Australia University of Leeds United Kingdom Leeds Teaching Hospitals Trust United Kingdom The Department of Computer Science Loughborough University United Kingdom

Background - Physiological tremor is defined as an involuntary and rhythmic shaking. Tremor of the hand is a key symptom of multiple neurological diseases, and its frequency and amplitude differs according to both disease type and disease progression. In routine clinical practice, tremor frequency and amplitude are assessed by expert rating using a 0 to 4 integer scale. Such ratings are subjective and have poor inter-rater reliability. There is thus a clinical need for a practical and accurate method for objectively assessing hand tremor. Objective - to develop a proof-of-principle method to measure hand tremor amplitude from smartphone videos. methods - We created a computer vision pipeline that automatically extracts salient points on the hand and produces a 1-D time series of movement due to tremor, in pixels. Using the smartphones’ depth measurement, we convert this measure into real distance units. We assessed the accuracy of the method using 60 videos of simulated tremor of different amplitudes from two healthy adults. Videos were taken at distances of 50, 75 and 100 cm between hand and camera. The participants had skin tone II and VI on the Fitzpatrick scale. We compared our method to a gold-standard measurement from a slide rule. Bland-Altman methods agreement analysis indicated a bias of 0.04 cm and 95% limits of agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that the method was robust to differences in skin tone and limited occlusion, such as a band-aid affixed to the participant’s hand. Clinical relevance - We have demonstrated how tremor amplitude can be measured from smartphone videos. In conjunction with tremor frequency, this approach could be used to help diagnose and monitor neurological diseases. © 2023, CC BY.

关键词： Smartphones

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Reward-based Charging Schedule for a Community-based Ride-sharing Service

Reward-based Charging Schedule for a Community-based Ride-sh...

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Smart Mobility (SM), IEEE International Conference on

作者： Avinash Nagarajan Alan McGibney Pio Fenton Ignacio Castiñeiras Dept. of Computer Science Munster Technological University Cork Ireland NIMBUS Centre Munster Technological University Cork Ireland Teaching & Learning Unit Munster Technological University Cork Ireland

ISBN: (数字)9798350390131

ISBN: (纸本)9798350390148

This paper presents the design, implementation, and evaluation of a scalable charging scheduling algorithm for a proactive, Smart Energy Community-based, carbon-neutral ridesharing service. The reward-based algorithm incorporates multiple constraints, including energy usage, vehicle distribution, route planning, passenger-to-vehicle matching, and charging station availability. The solution approach builds on considerations from a previous study on a carbon-neutral, Smart Energy Community-based, reactive ride-sharing service, ensuring efficient scheduling of electric vehicle chargings. The results reveal up to a 10% improvement in the number of trips being served, concluding that the proposed algorithm contributes to the optimisation of the ride-sharing service, while maintaining its efficiency and scalability.

关键词： Schedules Shared transport Scheduling algorithms Scalability Charging stations Electric vehicle charging Optimization

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Dyn-GCN: Graph Embedding via Dynamic Evolution and Graph Convolutional Network for Personal Recommendation

Dyn-GCN: Graph Embedding via Dynamic Evolution and Graph Con...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Wang, Zhihui Chen, Jianrui Wang, Peijie Zhu, Tingting School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Modem Teaching Technology Ministry of Education Xi'an China Pakistan

ISBN: (纸本)9781665441582

Graph convolutional network (GCN) and dynamic evolutionary model are the mainstream collaborative filtering technologies in recent years. Nevertheless, the initial feature vectors selection problem of the existing recommendation algorithms based on dynamic evolutionary models leads to unstable recommendation accuracy. In addition, the collaborative filtering method of GCN does not take into account the dynamic evolution law of graph networks. Based on this, this research adopts GCN to train the initial embedding of the dynamic evolution model to perform collaborative filtering recommendation. First of all, a heterogeneous graph network is constructed by applying explicit feedback information (rating scores) of users. Secondly, the embedding of users and items are propagated through the dynamic evolution model. Then, the final embedding is obtained by weighting the embedding of each layer, and the scores are predicted. Finally, according to the Adam optimizer, the initial embedding of the dynamic evolution model is trained in the form of mini-batch to minimize the loss function. Experimental results show that the proposed algorithm is superior to several compared excellent algorithms in recommendation performance. © 2021 IEEE.

关键词： Embeddings

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An In-Depth Analysis of Data Reduction methods for Sustainable Deep Learning

arXiv

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arXiv 2024年

作者： Perera-Lago, Javier Toscano-Durán, Víctor Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutierrez-Naranjo, Miguel Ángel Rucco, Matteo Department of Applied Mathematics I University of Seville Seville Spain Department of Quantitative Methods University of Loyola Campus Sevilla Dos Hermanas Seville Spain Department of Computer Science and Artificial Intelligence University of Seville Seville Spain Data Science Department Biocentis Milan Italy

In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure how similar are the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model. © 2024, CC BY.

关键词： Energy efficiency

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Learning Superconductivity from Ordered and Disordered Material Structures 38

Learning Superconductivity from Ordered and Disordered Mater...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Pin Peng, Luoxuan Jiao, Rui Mo, Qing Wang, Zhen Huang, Wenbing Liu, Yang Lu, Yutong National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates. © 2024 Neural information processing systems foundation. All rights reserved.

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A Consensus-Based Multi-Criteria Group Decision-Making Method Based on an Aggregated Operator Customised by Experts

A Consensus-Based Multi-Criteria Group Decision-Making Metho...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： José Ramón Trillo Francisco Javier Cabrerizo María José Del Moral Juan Antonio Morente-Molinera Juan Miguel Tapia Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada Granada Spain University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

Group decision-making is a daily process where a group of experts has to choose from a set of alternatives. To help this group of experts, methods were developed that allowed them to recommend an alternative or a group of alternatives from the information generated by the experts, these methods were called Group Decision-Making methods. Nonetheless, some Group Decision-Making methods have some problems, such as evaluating alternatives in a general way. This problem leads to a loss of information because it is not possible to evaluate each alternative in detail and it may be the case that one alternative is better than another on one criterion but not on another. Another problem is that experts have priority for each criterion and for one expert one criterion may be more important than another and vice versa. To solve these two problems, in this novel method, we propose a consensual multi-criteria Group Decision-Making system, where experts can customise the order of the criteria. With the order of each expert, the method creates an aggregation operator for each criterion. Moreover, this method performs a consensus analysis for each criterion, so that there is a minimum consensus on each criterion. In this way, a solution is obtained that allows the set of alternatives to be evaluated in a detailed way and each criterion has the correct importance because the experts are the ones who create the order of importance.

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Instrumental Variables in Causal Inference and Machine Learning: A Survey

arXiv

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arXiv 2022年

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Wu, Fei The College of Computer Science and Technology Zhejiang University China The Department of Quantitative Theory & Methods Emory University United States

Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://***/causal-machine-learning-lab/mliv. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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CECILIA: Comprehensive Secure Machine Learning Framework

arXiv

引用

arXiv 2022年

作者： Ünal, Ali Burak Pfeifer, Nico Akgün, Mete Medical Data Privacy and Privacy-preserving ML Dept. of Computer Science University of Tuebingen Tübingen Germany Methods in Medical Informatics Dept. of Computer Science University of Tuebingen Tübingen Germany

Since machine learning algorithms have proven their success in many different applications, there is also a big interest in privacy preserving machine learning methods for building models on sensitive data. Moreover, the increase in the number of data sources and the high computational power required by those algorithms force individuals to outsource the training and/or the inference of a machine learning model to the clouds providing such services. To address this, we propose a secure three party computation framework, CECILIA, offering privacy preserving building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix. We use CECILIA to realize the private inference on pre-trained recurrent kernel networks, which require more complex operations than most other deep neural networks, on the structural classification of proteins as the first study ever accomplishing the privacy preserving inference on recurrent kernel networks. In addition to the successful private computation of basic building blocks, the results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, they also show that we compute the exact inverse square root of a secret Gram matrix up to a certain privacy level, which has not been addressed in the literature at all. We also analyze the scalability of CECILIA to various settings on a synthetic dataset. The framework shows a great promise to make other machine learning algorithms as well as further computations privatel

关键词： Adversarial machine learning

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