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arXiv 2022年

作者： Tushar, Tahrim Faroque Yang, Yan Hossain, Zakir Naim, Sheikh Motahar Mohammed, Nabeel Rahman, Shafin Department of Electrical and Computer Engineering North South University Bangladesh Biological Data Science Institute The Australian National University Canberra Australia CSIRO Agriculture & Food Canberra Australia CSIRO Machine Learning & Artificial Intelligence Future Science Platform Canberra Australia Amazon Web Services United States

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or considered raw smile videos in an end-to-end manner to perform smile classification tasks. Feature-based methods require intervention from human experts on feature engineering and heavy pre-processing steps. On the contrary, raw smile video inputs fed into end-to-end models bring more automation to the process with the cost of considering many redundant facial features (beyond landmark locations) that are mainly irrelevant to smile veracity classification. It remains unclear to establish discriminative features from landmarks in an end-to-end manner. We present a MeshSmileNet framework, a transformer architecture, to address the above limitations. To eliminate redundant facial features, our landmarks input is extracted from Attention Mesh, a pre-trained landmark detector. Again, to discover discriminative features, we consider the relativity and trajectory of the landmarks. For the relativity, we aggregate facial landmark that conceptually formats a curve at each frame to establish local spatial features. For the trajectory, we estimate the movements of landmark composed features across time by self-attention mechanism, which captures pairwise dependency on the trajectory of the same landmark. This idea allows us to achieve state-of-the-art performances on UVA-NEMO, BBC, MMI Facial Expression, and SPOS datasets. © 2022, CC BY-NC-ND.

关键词： Relativity

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FXAM: A Unified and Fast Interpretable Model for Predictive Analytics

SSRN

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SSRN 2023年

作者： Jiang, Yuanyuan Ding, Rui Qiao, Tianchi Zhu, Yunan Han, Shi Zhang, Dongmei School of Statistics Renmin University of China Haidian District Beijing100872 China Microsoft Research Asia Haidian District Beijing100080 China School of Computer Science and Engineering Southeast University Jiangsu Province Nanjing211189 China Huazhong University of Science and Technology China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's inherent interpretability for predictive analytics. © 2023, The Authors. All rights reserved.

关键词： machine learning

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CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases

CBAM_SAUNet: A novel attention U-Net for effective segmentat...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Srividya Tirunellai Rajamani Kumar Rajamani Angeline J Karthika R Björn W. Schuller Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Rajkot Gujarat India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India CHI - Chair of Health Informatics MRI Technische Universität München (TUM) Germany Munich Data Science Institute Germany Munich Center for Machine Learning and GLAM - the Group on Language Audio & Music Imperial College London London UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)’s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

关键词： Deep learning Image segmentation Attention mechanisms Shape Magnetic resonance imaging Myocardium Decoding Medical diagnostic imaging

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Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium 15th

Automated Pipeline for Regional Epicardial Adipose Tissue Di...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Lluch, Èric Castañeda, Eduardo Weiss, Maximilian E. R. Vizitiu, Anamaria Jacob, Athira Jami, Jitin Audigier, Chloé Meister, Felix Mihalef, Viorel Passerini, Tiziano Siemens Healthineers Digital Technologies and Innovation Erlangen Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Siemens SRL Brasov Romania Siemens Healthineers Digital Technology and Innovation Princeton United States Machine-Learning and Data Analytics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Advanced Clinical Imaging Technology Siemens Healthineers AG Lausanne Switzerland

ISBN: (纸本)9783031877551

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Rationalising data collection for supporting decision making in building energy systems using Value of Information analysis

arXiv

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arXiv 2024年

作者： Langtry, Max Zhuang, Chaoqun Ward, Rebecca Makasis, Nikolas Kreitmair, Monika J. Conti, Zack Xuereb Di Francesco, Domenic Choudhary, Ruchi Energy Efficient Cities Initiative Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Data-Centric Engineering The Alan Turing Institute British Library LondonNW1 2DB United Kingdom School of Sustainability Civil & Environmental Engineering University of Surrey GuilfordGU2 7XH United Kingdom Computational Statistics & Machine Learning Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom

The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economised and prioritised, avoiding wastage. © 2024, CC BY.

关键词： Indoor air pollution

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Convergence of Multiscale Reinforcement Q-learning Algorithms for Mean Field Game and Control Problems

arXiv

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arXiv 2023年

作者： Angiuli, Andrea Fouque, Jean-Pierre Laurière, Mathieu Zhang, Mengrui Prime Machine Learning Team Amazon. 320 Westlake Ave N SEA83 SeattleWA98109 United States Department of Statistics and Applied Probability South Hall University of California Santa BarbaraCA93106 United States Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai NYU Shanghai 567 West Yangsi Road Shanghai200126 China

We establish the convergence of the unified two-timescale Reinforcement learning (RL) algorithm presented in [Angiuli et al., 2022]. This algorithm provides solutions to Mean Field Game (MFG) or Mean Field Control (MFC) problems depending on the ratio of two learning rates, one for the value function and the other for the mean field term. Our proof of convergence highlights the fact that in the case of MFC several mean field distributions need to be updated and for this reason we present two separate algorithms, one for MFG and one for MFC. We focus on a setting with finite state and action spaces, discrete time and infinite horizon. The proofs of convergence rely on a generalization of the two-timescale approach of [Borkar, 1997]. The accuracy of approximation to the true solutions depends on the smoothing of the policies. We provide a numerical example illustrating the convergence. © 2023, CC BY.

关键词： Reinforcement learning

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy Raju T. Sajana V. Vijayalakshmi JoshuvaArockia Dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

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Exploration of Process Mining Opportunities In Educational Software Engineering - The GitLab Analyser 13

Exploration of Process Mining Opportunities In Educational S...

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13th International Conference on Educational data Mining, EDM 2020

作者： Dumbach, Philipp Aly, Alexander Zrenner, Markus Eskofier, Bjoern M. Machine Learning and Data Analytics Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Carl-Thiersch-Str. 2b Erlangen91052 Germany

ISBN: (纸本)9781733673617

The increasing complexity in software development leads to the necessity for a detailed data analysis. Literature illustrates a stronger research focus on Educational Process Mining (EPM) being applied to the fields of e-learning and professional training. In this work, the opportunities of Process Mining (PM) are further examined by the evaluation of software engineering (SE) courses. The methodology follows the five stages of the L* life cycle model for PM projects using data from software repositories. The event log data was analyzed with the PM tool Disco to examine the students’ work following an agile development process. The new tool GitLab Analyser supports supervisors to visualize educational processes and still extracts event logs for the further analysis and application of PM techniques. © 2020 Proceedings of the 13th International Conference on Educational data Mining, EDM 2020. All rights reserved.

关键词： data mining

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