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Ultrafast Online Clutter Filtering for Ultrasound Microvascular Imaging

IEEE Transactions on Medical Imaging 2025年第6期44卷 2477-2491页

作者： Chen, Yinran Fang, Baohui Li, Huaying Huang, Lijie Luo, Jianwen Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Tsinghua University School of Biomedical Engineering Beijing100084 China

Spatiotemporal clutter filtering via robust principal component analysis (rPCA) has been widely used in ultrasound microvascular imaging. However, the performance of the rPCA clutter filtering highly relies on low-rank modeling for tissue signals and sparse modeling for blood flow signals. Moreover, current rPCA clutter filters are typically based on static processing and have to access a batch of beamformed frames for optimization. This prevents these filters from ultrafast realization. This paper adopts the iteratively reweighted least squares (IRLS) rPCA framework to model tissue and blood flow signals for improved clutter filtering. More importantly, the static IRLS-rPCA filter is upgraded to a spatiotemporal-constrained online method to instantaneously extract blood flow signals from the ongoing beamformed frame. Simulations and in-vivo experiments on a contrast-enhanced rat kidney and a contrast-free human liver demonstrated that the IRLS-rPCA clutter filter achieves higher sensitivity, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) than other rPCA methods. Particularly, the static IRLS-rPCA clutter filter obtains more than 2 dB improvements in CNR over the compared methods in the human liver dataset. The proposed online clutter filter achieves comparable image quality to the static version and processing time of 0.028 ± 0.004 seconds per frame. The corresponding acceleration factor of the online clutter filter over all the tested methods is more than 20. © 2025 IEEE.

关键词： Radar clutter

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Multimodal Contrastive Learning for Protein-Protein Interaction Inhibitor Prediction

Multimodal Contrastive Learning for Protein-Protein Interact...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Zitong Wang, Zhixian Zhao, Lingling Wang, Junjie Wang, Chunyu Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350386226

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://***/1zzt/MCLPPII. © 2024 IEEE.

关键词： Contrastive Learning

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Cervical Cancer Histopathological Image Classification Using Imbalanced Domain Learning 11th

Cervical Cancer Histopathological Image Classification Using...

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11th EAI International Conference on Advancement of Science and Technology, ICAST 2023

作者： Simegn, Gizeaddis Lamesgin Degu, Mizanu Zelalem Tegenaw, Geletaw Sahle Biomedical Imaging Unit School of Biomedical Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia AI and Biomedical Imaging Research Unit Jimma Institute of Technology Jimma University Jimma Ethiopia Faculty of computing and informatics Jimma Institute of Technology Jimma University Jimma Ethiopia

ISBN: (纸本)9783031641503

Early diagnosis of cervical cancer is challenging, especially in developing countries due to limited resource, infrastructure, and experts. Recent improvements in deep learning-based early cervical cancer detection techniques can potentially lower these challenges while increasing diagnosis accuracy. In this study, experiments were conducted on 465 histopathological images collected from patients with adenocarcinoma, squamous cell carcinoma, and squamous intraepithelial lesion. CNN and ResNet-50 models were trained for classification after the application of data augmentation, color normalization, and synthetic minority oversampling technique (SMOTE) on the data. Classification accuracies of 86.43% and 92.14% were found using CNN and ResNet-50 trained on raw data, respectively. ResNet-50 trained on a dataset "without a preprocessing technique," "affine transform-based data augmentation," "data augmentation and histogram match-based color normalization," "SMOTE," and "color normalization and SMOTE" achieved accuracies of 94.20%, 96.39%, 95.32%, 99.14%, and 90.56%, respectively. In general, the study investigated how image preprocessing, specifically data balancing, influences the effectiveness of deep learning models. Additionally, it examined the possibilities of utilizing computer-aided image processing to detect cervical cancer. The findings show that, with proper data processing, the proposed technique might be used as a cervical cancer screening tool, particularly in low-resource settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Affine transforms

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Digital Clinical Decision Support System for Screening of Eye Diseases 10th

Digital Clinical Decision Support System for Screening of Ey...

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10th EAI International Conference on Advancements of Science and Technology, EAI ICAST 2022

作者： Simegn, Gizeaddis Lamesgin Degu, Mizanu Zelalem Biomedical Imaging Unit School of Biomedical Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia AI and Biomedical Imaging Research Unit Jimma Institute of Technology Jimma University Jimma Ethiopia Faculty of Computing and Informatics Jimma Institute of Technology Jimma University Jimma Ethiopia

ISBN: (纸本)9783031411724

Eye diseases are a major cause of blindness, primarily affecting elderly individuals, especially in developing nations. Among various eye diseases, glaucoma, age-related macular degeneration, and cataracts are the most prevalent in sub-Saharan African nations. Many of these eye conditions are preventable or treatable if detected early. Unfortunately, in low-resource settings, due to a lack of screening mechanisms and poorly organized healthcare structures, people do not often seek early checkups before the disease progresses. Furthermore, most eye diseases do not exhibit clear symptoms in the early stages and may be irreversible. Developing countries’ health facilities lack modern and high-quality screening technologies and specialized experts. In addition, conventional eye screening methods are reliant on physicians’ expertise and knowledge, which can lead to misdiagnosis. This study proposes a digital eye disease diagnosis support system that integrates image acquisition, enhancement, retinal vessel extraction, machine learning-based segmentation, and cup-to-disk ratio determination with intraocular pressure measurement for automated eye disease screening. Our experimental results indicate that this proposed system has the potential to be utilized as a decision support system for eye disease diagnosis, particularly in resource-limited settings. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cup-to-disk ratio Eye disease Glaucoma Image processing Intraocular pressure measurement Machine learning Segmentation Vessel extraction

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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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Computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Combining Machine Learning and Network Analysis Pipelines: The Case of Microbiome and Metabolomics Data in Colorectal Cancer

Combining Machine Learning and Network Analysis Pipelines: T...

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作者： Ntzioni, Eleni Chouvarda, Ioanna Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781643682501

This study analyzes samples of intestinal microbiome and metabolites, from healthy individuals (HE) and patients with adenomas (AD) or colorectal carcinomas (CRC). A network analysis (NetAn) method was applied to the data, to identify the metabolites and microbial genera associated with the 3 classes and then 7 classification models were used. The models were initially trained with classic feature selection vs features resulting from NetAn. The distinction of HE and AD is successful, while CRC distinction presented lower success. © 2022 The authors and IOS Press.

关键词： Diseases

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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