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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

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

arXiv

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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

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Large language models:game-changers in the healthcare industry

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Science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

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Ai-Driven Automated Tool for Abdominal CT Body Composition analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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IEEE International Symposium on Biomedical Imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang 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 Beijing International Center for Mathematical Research (BICMR) 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

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Enhancing Predictive Imaging Biomarker Discovery Through Treatment Effect analysis

Enhancing Predictive Imaging Biomarker Discovery Through Tre...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shuhan Xiao Lukas Klein Jens Petersen Philipp Vollmuth Paul F. Jaeger Klaus H. Maier-Hein German Cancer Research Center (DKFZ) Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany DKFZ Heidelberg Interactive Machine Learning Group Germany Institute for Machine Learning ETH Zürich Switzerland DKFZ Heidelberg Helmholtz Imaging Germany Division for Computational Radiology Clinical AI (CCIBonn.ai) Clinic for Neuroradiology University Hospital Bonn Germany Medical Faculty Bonn University of Bonn Germany Department of Radiation Oncology Pattern Analysis and Learning Group Heidelberg University Hospital Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis.

关键词： Analytical models Statistical analysis Biological system modeling Precision medicine Decision making Imaging Estimation Biomarkers Predictive models Feature extraction

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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An Empirical Study of Super-Resolution on Low-Resolution Micro-Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Zhou, Ling Wang, Mingpei Huang, Xiaohua Zheng, Wenming Mao, Qirong Zhao, Guoying Macau University of Science and Technology School of Computer Science and Engineering 999078 China Nanjing Institute of Technology School of International Education Oulu School Jiangsu Nanjing211167 China Southeast University Key Laboratory of Child Development and Learning Science Ministry of Education Nanjing210096 China Southeast University Research Center for Learning Science China Southeast University School of Biological Science and Medical Engineering Jiangsu Nanjing210096 China Jiangsu University School of Computer Science and Communication Engineering Jiangsu Zhenjiang212013 China University of Oulu Center for Machine Vision and Signal Analysis 90014 Finland

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution (SR) techniques for enhancing the quality of LR images and videos, few study has focused on investigate SR for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of SR-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both SR and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 22 SOTA SR techniques, thereby addressing the problem of SR in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER. © 2010-2012 IEEE.

关键词： Optical resolving power

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Flow-Based Sampling for Entanglement Entropy and the machine learning of Defects

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Physical Review Letters 2025年第15期134卷 151601-151601页

作者： Andrea Bulgarelli Elia Cellini Karl Jansen Stefan Kühn Alessandro Nada Shinichi Nakajima Kim A. Nicoli Marco Panero Department of Physics University of Turin and INFN Turin unit Via Pietro Giuria 1 I-10125 Turin Italy Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Deutsches Elektronen-Synchrotron DESY Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany RIKEN Center for AIP Tokyo Japan Transdisciplinary Research Area (TRA) Matter University of Bonn Germany Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Bonn Germany Department of Physics and Helsinki Institute of Physics PL 64 FIN-00014 University of Helsinki Finland

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

关键词： Lattice theory

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Ai...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Iosif Tsangko Andreas Triantafyllopoulos Michael Müller Hendrik Schröter Björn W. Schuller EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics Technical University of Munich Germany MCML – Munich Center for Machine Learning Munich Germany WS Audiology Research and Development Erlangen Germany GLAM – Group on Language Audio & Music Imperial College London UK MDSI – Munich Data Science Institute Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a ‘one-size-fits-all’ approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mitigate this. These recordings can be offloaded outside the hearing aid, thus improving performance while adding minimal computational overhead. We introduce these principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which shows superior performance on various benchmarks inspired by the DNS Challenge.

关键词： Performance evaluation Adaptation models Computational modeling Noise Noise reduction Computer architecture Speech enhancement Fingerprint recognition Hearing aids Recording

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