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Biophotonics in Exercise science, Sports Medicine, Health Monitoring Technologies, and Wearables VI 2025

作者： Xue, Minghao Huang, Wei-Lun Tashayyod, Davood Kang, Jun Gandjbakhche, Amir Armand, Mehran Johns Hopkins University Whiting School of Engineering Department of Computer Science BaltimoreMD United States Johns Hopkins School of Medicine Department of Dermatology BaltimoreMD United States Eunice Kennedy Shriver National Institute of Child Health and Human Development BethesdaMD United States University of Arkansas Institute for Integrative and Innovative Research Department of Mechanical Engineering AR United States Lumo Imaging RockvilleMD United States

ISBN: (纸本)9781510683747

Total-body photography (TBP) has the potential to revolutionize early detection of skin cancers by monitoring minute changes in lesions over time. However, there is no standardized Digital Imaging and Communications in Medicine (DICOM) format for TBP. In order to accommodate various TBP data types and sophisticated data preprocessing pipelines, we propose three TBP Extended Information Object Definitions (IODs) for 2D regional images, dermoscopy images, and 3D surface meshes. We introduce a comprehensive pipeline integrating advanced image processing techniques, including 3D DICOM representation, super-resolution enhancement, and style transfer for dermoscopic-like visualization. Our framework tracks individual lesions across multiple TBP scans from different imaging systems and provides cloud-based storage with a customized DICOM viewer. To demonstrate the effectiveness of our approach, we validate our framework using TBP datasets from multiple imaging systems. Our framework and proposed IODs enhance TBP interoperability and clinical utility in dermatological practice, potentially improving early skin cancer detection. © 2025 SPIE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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“I Want to Think Like an SLP”: A Design Exploration of AI-Supported Home Practice in Speech Therapy 25

“I Want to Think Like an SLP”: A Design Exploration of AI-...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Aayushi Dangol Aaleyah Lewis Hyewon Suh Xuesi Hong Hedda Meadan James Fogarty Julie A. Kientz Human Centered Design & Engineering University of Washington SEATTLE Washington USA Paul G. Allen School of Computer Science and Engineering University of Washington Seattle Washington USA Human Centered Design & Engineering University of Washington Seattle Washington USA Global Innovation Exchange University of Washington SEATTLE Washington USA Department of Special Education and Child Development University of North Carolina at Charlotte Charlotte North Carolina USA Human Centered Design and Engineering University of Washington Seattle Washington USA

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The Promise of Investigating Neural Variability in Psychiatric Disorders

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Biological Psychiatry 2025年

作者： Tsikonofilos, Konstantinos Kumar, Arvind Ampatzis, Konstantinos Garrett, Douglas D. Månsson, Kristoffer N.T. Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden Department of Neuroscience Karolinska Institutet Stockholm Sweden Division of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany and London United Kingdom Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Department of Clinical Psychology and Psychotherapy Babeș-Bolyai University Cluj-Napoca Romania

Researchers have begun to use the synergy of psychiatry and neuroscience to identify biomarkers that can be used to diagnose mental health disorders, predict their progression, and forecast treatment efficacy. However, biomarkers have achieved limited success to date, potentially due to a narrow focus on specific aspects of brain signals. This highlights a critical need for methodologies that can fully exploit the potential of neuroscience to transform psychiatric practice. In recent years, there has been emerging evidence of the ubiquity and importance of moment-to-moment neural variability for brain function. Single-neuron recordings and computational models have demonstrated the significance of variability even at the microscopic level. Concurrently, studies involving healthy humans using neuroimaging recording techniques have strongly indicated that neural variability, which in the past was dismissed as undesirable noise, is an important substrate for cognition. Given the cognitive disruption seen in several psychiatric disorders, neural variability is a promising biomarker in this context, and careful consideration of design choices is necessary to advance the field. In this review, we provide an overview of the significance and substrates of neural variability across different recording modalities and spatial scales. We also review the existing evidence that supports its relevance in the study of psychiatric disorders. Finally, we advocate for future research to investigate neural variability within disorder-relevant, task-based paradigms and longitudinal designs. Supported by computational models of brain activity, this framework holds the potential for advancing precision psychiatry in a powerful and experimentally feasible manner. © 2025 Society of Biological Psychiatry

关键词： Biomarkers Computational models Neural variability Psychiatry Task-based design

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Correction: Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

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International journal of obesity (2005) 2025年第2期49卷 364-365页

作者： Arthur Leroy Varsha Gupta Mya Thway Tint Delicia Shu Qin Ooi Fabian Yap Ngee Lek Keith M Godfrey Yap Seng Chong Yung Seng Lee Johan G Eriksson Mauricio A Álvarez Navin Michael Dennis Wang Department of Computer Science The University of Manchester Manchester UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Department of Paediatrics KK Women's and Children's Hospital Singapore Republic of Singapore. Duke-NUS Medical School Singapore Republic of Singapore. MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK. Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Republic of Singapore. Division of Paediatric Endocrinology Department of Paediatrics Khoo Teck Puat-National University Children's Medical Institute National University Hospital National University Health System Singapore Republic of Singapore. Department of General Practice and Primary Health Care University of Helsinki Helsinki Finland. Folkhälsan Research Center Helsinki Finland. Department of Computer Science University of Sheffield Sheffield UK. Institute for Human Development and Potential Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Bioinformatics Institute Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore. dennis_wang@sics.a-star.edu.sg. Department of Computer Science University of Sheffield Sheffield UK. dennis_wang@sics.a-star.edu.sg. National Heart and Lung Institute Imperial College London London UK. dennis_wang@sics.a-star.edu.sg.

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DP-NMT: Scalable Differentially-Private Machine Translation 18

DP-NMT: Scalable Differentially-Private Machine Translation

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024

作者： Igamberdiev, Timour Vu, Doan Nam Long Künnecke, Felix Yu, Zhuo Holmer, Jannik Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany Trustworthy Human Language Technologies Department of Computer Science Paderborn University Germany

ISBN: (纸本)9798891760912

Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees;however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhanced Identification of Pheochromocytoma and Paragangliomas’ Genetic Clusters from CT

Enhanced Identification of Pheochromocytoma and Paragangliom...

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Medical Imaging 2025: computer-Aided Diagnosis

作者： Makroo, Owais Santra, Bikash Mukherjee, Pritam Mathai, Tejas Sudharshan Jha, Abhishek Patel, Mayank Pacak, Karel Summers, Ronald M. Department of Mathematics Indian Institute of Technology West Bengal Kharagpur India School of AI and Data Science Indian Institute of Technology Rajasthan Jodhpur India Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Radiology and Imaging Sciences Clinical Center National Institutes of Health BethesdaMD United States National Institute of Child Health and Human Development National Institutes of Health BethesdaMD United States

ISBN: (纸本)9781510685925

Pheochromocytoma and Paraganglioma (PPGL) tumors are uncommon neuroendocrine tumors that exhibit substantial genetic and phenotypic variability, making their classification challenging. We describe a new method that performs supervised contrastive learning leveraging DinoV2 feature representations to categorize genetic clusters of PPGL tumors into four distinct subtypes: SDHx, VHL-EPAS1, kinase signaling, and sporadic. The proposed supervised contrastive learning framework teaches a shallow linear network using feature representations taken from a DinoV2 model that has already been pre-trained with ImageNet-21K. This training process optimizes both contrastive and classification losses together, taking into account the genetic groups and four anatomical placements of PPGLs: head and neck, chest, adrenal, and extra-adrenal abdomen and pelvis. The proposal was tested on a dataset consisting of 650 contrast-enhanced computed tomography (CE-CT) axial images of 287 PPGL patients. The results showed a balanced accuracy of 0.582±0.079, an AUC score of 0.805±0.055, and an F1 score of 0.551±0.063 using five-fold cross-validation. Our methodology demonstrated superior performance compared to other methods by balancing the performance across all four genetic types, achieving a 2% higher balanced accuracy and a 7–14% higher F1 score, while using the entire axial slice as a single sample in contrast to using the extracted tumor patches. Moreover, the sporadic cluster achieved the highest balanced accuracy (0.652±0.037), while the SDHx cluster had the highest F1 score (0.738±0.0565). Experimental studies have verified that incorporating predictions that are aware of specific regions during training, as well as utilizing supervised contrastive learning, are both essential for attaining these outcomes. © 2025 SPIE.

关键词： Support vector machines

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Effective fingerprint recognition approach based on double fingerprint thumb

Effective fingerprint recognition approach based on double f...

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2017 International Conference on Current Research in computer science and Information Technology, ICCIT 2017

作者： Al-Ani, Muzhir Shaban Muhamad, Tishko N. Muhamad, Hersh A. Nuri, Ayub A. Department of Computer Science University of Human Development Sulaimania Iraq

ISBN: (纸本)9781538629550

Many techniques are implemented to perform fingerprint recognition approach, and each technique based on specific criteria. The aim of this work is to find an efficient fingerprint recognition technique. This paper tries to offer a simple high performance approach to perform fingerprint recognition. This approach based on two main stages;the first one is the real data collection of human fingerprint samples and the second stage is concentrated on design and implementation of high performance fingerprint recognition approach. The implemented approach concentrated on the feature extraction part in which many levels of two dimensional discrete wavelet transform (2D-DWT) are used to generate high performance feature. This approach is implemented via mixing the features of both right hand thumb and left hand thumb. The results indicate that a good accuracy of recognition is obtained by this approach. © 2017 IEEE.

关键词： Discrete wavelet transforms

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A lightweight algorithm against replica node attack in mobile wireless sensor networks using learning agents

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IEIE Transactions on Smart Processing and Computing 2019年第1期8卷 58-70页

作者： Jamshidi, Mojtaba Poor, Shokooh Sheikh Abooli Qader, Nooruldeen Nasih Esnaashari, Mehdi Meybodi, Mohammad Reza Department of Information Technology University of Human Development Sulaymaniyah Iraq Ahvaz Iran Computer Science Department University of Human Development Sulaymaniyah Iraq Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran

The node replication attack is considered one of the most dangerous attacks against wireless sensor networks (WSNs). In this attack, an adversary captures one or more normal nodes of the network, extracts its key materials, generates several replicas, and deploys them in the network. In this paper, we propose a novel, intelligent, and lightweight algorithm using learning agents and watchdog nodes (WNs) to detect replica nodes in mobile WSNs. In the proposed algorithm, there are a few WNs, each one equipped with a learning agent that monitors the network traffic and nodes' movements to identify potential replica nodes in the network. We use the fact that the number of times that a replica node meets a WN is probably more than that of a normal node during a specific monitoring interval of the network. The proposed algorithm is simulated using the J-SIM simulator, and its performance is evaluated in terms of replica node-detection probability and false detection probability through experiments. Experiment results show that the proposed algorithm can detect 100% of the replica nodes, while its false detection probability is less than 0.01. Copyrights © 2019 The Institute of Electronics and Information Engineers.

关键词： Learning algorithms

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One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks

One size does not fit all: Investigating strategies for diff...

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2022 Conference on Empirical Methods in Natural language Processing, EMNLP 2022

作者： Senge, Manuel Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany

Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (DP-SGD) generally degrade model performance. However, previous research on the efficiency of DP-SGD in NLP is inconclusive or even counter-intuitive. In this short paper, we provide an extensive analysis of different privacy preserving strategies on seven downstream datasets in five different 'typical' NLP tasks with varying complexity using modern neural models based on BERT and XtremeDistil architectures. We show that unlike standard non-private approaches to solving NLP tasks, where bigger is usually better, privacy-preserving strategies do not exhibit a winning pattern, and each task and privacy regime requires a special treatment to achieve adequate performance. © 2022 Association for Computational Linguistics.

关键词： Natural language processing systems

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