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arXiv 2025年

作者： Tassopoulou, Vasiliki Shou, Haochang Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Department of Bioengineering University of Pennsylvania United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States

Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories of brain biomarkers is challenging due to biological variability, inconsistencies in measurement protocols (e.g., differences in MRI scanners) as well as scarcity and irregularity in longitudinal measurements. Herein, we introduce a novel personalized deep kernel regression framework for forecasting brain biomarkers, with application to regional volumetric measurements. Our approach integrates two key components: a population model that captures brain trajectories from a large and diverse cohort, and a subject-specific model that captures individual trajectories. To optimally combine these, we propose Adaptive Shrinkage Estimation, which effectively balances population and subject-specific models. We assess our model’s performance through predictive accuracy metrics, uncertainty quantification, and validation against external clinical studies. Benchmarking against state-of-the-art statistical and machine learning models—including linear mixed effects models, generalized additive models, and deep learning methods—demonstrates the superior predictive performance of our approach. Additionally, we apply our method to predict trajectories of composite neuroimaging biomarkers, which highlights the versatility of our approach in modeling the progression of longitudinal neuroimaging biomarkers. Furthermore, validation on three external neuroimaging studies confirms the robustness of our method across different clinical contexts. We make the code available at https://***/vatass/AdaptiveShrinkageDKGP. © 2025, CC BY.

关键词： Aerobraking

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Preface for the 2nd International Workshop on Semantic Industrial Information Modelling (SemIIM) 2

Preface for the 2nd International Workshop on Semantic Indus...

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2nd International Workshop on Semantic Industrial Information Modelling, SemIIM 2023

作者： Waaler, Arild Kharlamov, Evgeny Zhou, Baifan Soylu, Ahmet Kyritsis, Dimitrios Roman, Dumitru Savkovic, Ognjen Staab, Steffen SIRIUS Centre Department of Informatics University of Oslo Norway Bosch Center for AI Germany Department of Computer Science Oslo Metropolitan University Norway SINTEF AS Norway Norway KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Italy Institute for Artificial Intelligence University of Stuttgart Germany

SemIIM2023 was a full-day workshop that took place on 7th November 2023 in Athens, Greece, colocated with the 22nd edition of the International Semantic Web Conference (ISWC2023). This workshop invited industrial as well as academic keynotes and papers, covering the challenges and solutions for addressing industrial information modelling, including methods and practices of representing concepts, relationships, constraints, rules and operations to specify data semantics for a chosen domain of interest. The workshop gathered the interested community and discussed the latest approaches for challenges both from the perspectives of academia and industry. © 2023 CEUR-WS. All rights reserved.

关键词： Semantic Web

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Silk Fibroin-Based Hydrogel for Multifunctional Wearable Sensors

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Journal of Renewable Materials 2022年第11期10卷 2729-2746页

作者： Yiming Zhao Hongsheng Zhao Zhili Wei Jie Yuan Jie Jian Fankai Kong Haojiang Xie Xingliang Xiong College of Medical Informatics Chongqing Medical UniversityChongqing400016China Medical Data Science Academy Chongqing Medical UniversityChongqing400016China Communication and Information Center of Ministry of Emergency Management Beijing100013China The Ministry of Education Key Laboratory of Laboratory Medical Diagnostics The College of Laboratory MedicineChongqing Medical UniversityChongqing400016China

The flexible wearable sensors with excellent stretchability,high sensitivity and good biocompatibility are significantly required for continuously physical condition tracking in health management and rehabilitation ***,we present a high-performance wearable *** sensor is prepared with nanocomposite hydrogel by using silk fibroin(SF),polyacrylamide(PAM),polydopamine(PDA)and graphene oxide(GO).It can be used to monitor body motions(including large-scale and small-scale motions)as well as human electrophysiological(ECG)signals with high sensitivity,wide sensing range,and fast response ***,the proposed sensor is promising in the fields of rehabilitation,motion monitoring and disease diagnosis.

关键词： Acrylic amide silk fibroin graphene oxide wearable sensor flexible strain sensor

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S2CCT: Self-Supervised Collaborative CNN-Transformer for Few-shot Medical Image Segmentation

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Rongzhou Shu, Ziqi Xie, Weixing Yao, Junfeng Hong, Qingqi Xiamen University National Institute for Data Science in Health and Medicine China Xiamen University Center for Digital Media Computing School of Film School of Informatics Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage Fujian and Taiwan Ministry of Culture and Tourism China Xiamen University Institute of Artificial Intelligence Xiamen361005 China

ISBN: (纸本)9798350386226

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S2CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks;(2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation;(3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S2CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon. © 2024 IEEE.

关键词： Image segmentation

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Adaptive Region-Oriented Masked Vision Retentive Network for Predicting Macrovascular Invasion in Hepatocellular Carcinoma

Adaptive Region-Oriented Masked Vision Retentive Network for...

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Medical Imaging 2025: Computer-Aided Diagnosis

作者： Takahashi, Kengo Inamori, Ryusei Ichiji, Kei Zhang, Zhang Zeng, Yuwen Homma, Noriyasu Department of Radiological Imaging and Informatics Tohoku University Graduate School of Medicine 2-1 Seiryo Aoba-ku Miyagi Sendai980-8575 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University 41 Kawauchi Aoba-ku Miyagi Sendai980-8576 Japan

ISBN: (纸本)9781510685925

The aim of the present study was to develop the Adaptive Region-Oriented Masked Vision Retentive Network (AROMA ViR) model, which can efficiently learn the morphological structures of the liver, to predict macrovascular invasion (MI) in hepatocellular carcinoma (HCC). Retrospective CT images were obtained from the University of Texas MD Anderson Cancer center, in accordance with The Cancer Imaging Archive data usage policy and restrictions. The image dataset comprised 51,968 slices taken during the arterial phase in 105 patients with HCC. We split the data patient-wise into training, validation, and test datasets in a 6:2:2 ratio after applying specific exclusion criteria. The AROMA ViR was designed to enhance the relevant areas in the retention map by incorporating spatial information for liver parenchyma, tumor, and portal vein. The model applied causal masks specialized for specific liver shapes for each slice image into retention encoders. We compared the proposed model with Residual Network 101, Vision Transformer, and Vision Retentive Network. We calculated the area under the receiver-operating characteristic curve (AUC-ROC) and that under the precision recall curve (AUC-PR). We also obtained accuracy, sensitivity, specificity, and F1-score using Youden’s index. AROMA ViR pretrained by ImageNet showed AUC-ROC of 0.860, AUC-PR of 0.790, accuracy of 0.853, sensitivity of 0.719, specificity of 0.902, and F1 score of 0.578. © 2025 SPIE.

关键词： Image segmentation

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console

arXiv

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arXiv 2024年

作者： Nath, Anindita Mwesigwa, Savannah Dai, Yulin Jiang, Xiaoqian Zhao, Zhongming Center for Precision Health McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences HoustonTX77030 United States

The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist’s’copilot’. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer’s disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC’s operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI’s HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. © 2024, CC BY-NC-SA.

关键词： Visualization

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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