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Motif Discovery Framework for Psychiatric EEG data Classification

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arXiv 2025年

作者： Kraljevska, Melanija Hlavackova-Schindler, Katerina Miklautz, Lukas Plant, Claudia Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Währingerstrasse 29 Vienna1090 Austria ds:UniVie University of Vienna Austria

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general. © 2025, CC BY.

关键词： Electroencephalography

A Review of machine learning Algorithms in Diabetes Management 4

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A Review of Machine Learning Algorithms in Diabetes Manageme...

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

Rapidvol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

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Rapidvol: Rapid Reconstruction of 3D Ultrasound Volumes from...

IEEE International Symposium on biomedical Imaging

作者： Mark C. Eid Pak-Hei Yeung Madeleine K. Wyburd João F. Henriques Ana I.L. Namburete Visual Geometry Group University of Oxford U.K. Oxford Machine Learning in Neuroimaging Lab University of Oxford U.K. College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Two-dimensional (2D) freehand ultrasonography is a widely used medical imaging modality, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to costly 3D ultrasound (US) scanners, 3D volumes can be artificially reconstructed from 2D scans, but this is usually prohibitively slow. Hence, we propose RapidVol: a neural representation framework to speed up slice-to-volume US reconstruction. We use tensor-rank decomposition to decompose the typical 3D volume into tri-planes, which are stored alongside a small neural network. With a set of 2D US scans and their estimated 3D orientation, RapidVol can achieve complete 3D reconstruction. To evaluate our method, we form reconstructions from real fetal brain scans, and then request novel cross-sectional views. Compared to prior fully implicit (e.g. neural radiance field) approaches, our method is over 3x quicker, 46% more accurate, and more robust to errors in pose estimation. We also demonstrate that further speed-up is achievable by reconstructing from a structural prior rather than from random initialisation.

关键词： Three-dimensional displays Accuracy Two-dimensional displays Pose estimation Ultrasonography Neural radiance field Probes Obstetrics Image reconstruction biomedical imaging

Cause or Trigger? From Philosophy to Causal Modeling

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arXiv 2025年

作者： Hlaváčková-Schindler, Kateřina Wöß, Rainer Pecorino, Vera Schindler, Philip Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Department of Physics and Astronomy University of Catania Catania Italy Faculty of Philosophy University of Vienna Vienna Austria

Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on characterizations that differentiate them from each other. We carry out a philosophical analysis of these differences. From this, we formulate a definition that clearly differentiates triggers from causes and can be used for causal reasoning in natural sciences. We propose a mathematical model and the Cause- Trigger algorithm, which, based on given data to observable processes, is able to determine whether a process is a cause or a trigger of an effect. The possibility to distinguish triggers from causes directly from data makes the algorithm a useful tool in natural sciences using observational data, but also for real-world scenarios. For example, knowing the processes that trigger causes of a tropical storm could give politicians time to develop actions such as evacuation the population. Similarly, knowing the triggers of processes that cause global warming could help politicians focus on effective actions. We demonstrate our algorithm on the climatological data of two recent cyclones, Freddy and Zazu. The Cause-Trigger algorithm detects processes that trigger high wind speed in both storms during their cyclogenesis. The findings obtained agree with expert knowledge. © 2025, CC BY.

关键词： Physical properties

Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems

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arXiv 2025年

作者： Lin, Zhidi Li, Ying Yin, Feng Maroñas, Juan Thiéry, Alexandre H. The Department of Statistics and Data Science National University of Singapore Singapore117546 Singapore The Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Machine Learning Group Universidad Autónoma de Madrid The Department of Quantitative Methods CUNEF University

Gaussian process state-space models (GPSSMs) have emerged as a powerful framework for modeling dynamical systems, offering interpretable uncertainty quantification and inherent regularization. However, existing GPSSMs face significant challenges in handling high-dimensional, non-stationary systems due to computational inefficiencies, limited scalability, and restrictive stationarity assumptions. In this paper, we propose an efficient transformed Gaussian process state-space model (ETGPSSM) to address these limitations. Our approach leverages a single shared Gaussian process (GP) combined with normalizing flows and Bayesian neural networks, enabling efficient modeling of complex, high-dimensional state transitions while preserving scalability. To address the lack of closed-form expressions for the implicit process in the transformed GP, we follow its generative process and introduce an efficient variational inference algorithm, aided by the ensemble Kalman filter (EnKF), to enable computationally tractable learning and inference. Extensive empirical evaluations on synthetic and real-world datasets demonstrate the superior performance of our ETGPSSM in system dynamics learning, high-dimensional state estimation, and time-series forecasting, outperforming existing GPSSMs and neural network-based methods in both accuracy and computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

IEEE International Symposium on biomedical Imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

AI-DRIVEN AUTOMATED TOOL FOR ABDOMINAL CT BODY COMPOSITION ANALYSIS IN GASTROINTESTINAL CANCER MANAGEMENT

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arXiv 2025年

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

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RVPD: An Automated System for Calculating the Tortuosity and...

IEEE International Symposium on biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

RDMP: A Reference Detection and Mask Propagation Pipeline for Numerous 3D Glomeruli Segmentation in Large Volumes

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RDMP: A Reference Detection and Mask Propagation Pipeline fo...

IEEE International Symposium on biomedical Imaging

作者： Ziling Lin Zehua Li Zifan Chen Yao Lu Fangxu Zhou Bin Dong Li Zhang Haifeng Li Center for Data Science Peking University Beijing China Peking University First Hospital Beijing China College of Future Technology Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quantitative analysis of numerous repetitive structures in large volumes is one of the most challenging tasks in biomedical imaging, primarily due to its intensive manual requirements. Kidney glomeruli exemplify this challenge due to their dispersion across the cortical region, high density and microscopic size. To address this, we present a Reference Detection and Mask Propagation (RDMP) pipeline that combines object detection with 2D and 3D mask propagation, gradually achieving efficient segmentation of the entire volume by using a 2D reference example. Our method demonstrates robust performance, achieving a Dice Similarity Coefficient (DSC) of 0.9096 on microCT data, and when generalized to Synchrotron Radiation X-ray (SRX) data, reaching a DSC of 0.8431 with only minimal bounding box fine-tuning. Overall, this pipeline shows strong potential for efficient, large-scale kidney analysis, significantly reducing labor demands and accelerating kidney disease research.

关键词： Image segmentation Three-dimensional displays Statistical analysis Microscopy Pipelines Object detection Manuals Kidney Synchrotron radiation biomedical imaging