IEEE Journal of Biomedical and Health Informatics最新文献_第9页

CATransformer: A Cycle-Aware Transformer for High-Fidelity ECG Generation From PPG. CATransformer：从 PPG 生成高保真心电图的周期感知变压器。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-17 DOI: 10.1109/JBHI.2024.3482853

Xiaoyan Yuan, Wei Wang, Xiaohe Li, Yuanting Zhang, Xiping Hu, M Jamal Deen

{"title":"CATransformer: A Cycle-Aware Transformer for High-Fidelity ECG Generation From PPG.","authors":"Xiaoyan Yuan, Wei Wang, Xiaohe Li, Yuanting Zhang, Xiping Hu, M Jamal Deen","doi":"10.1109/JBHI.2024.3482853","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3482853","url":null,"abstract":"Electrocardiography (ECG) is the gold standard for monitoring heart function and is crucial for preventing the worsening of cardiovascular diseases (CVDs). However, the inconvenience of ECG acquisition poses challenges for long-term continuous monitoring. Consequently, researchers have explored non-invasive and easily accessible photoplethysmography (PPG) as an alternative, converting it into ECG. Previous studies have focused on peaks or simple mapping to generate ECG, ignoring the inherent periodicity of cardiovascular signals. This results in an inability to accurately extract physiological information during the cycle, thus compromising the generated ECG signals' clinical utility. To this end, we introduce a novel PPG-to-ECG translation model called CATransformer, capable of adaptive modeling based on the cardiac cycle. Specifically, CATransformer automatically extracts the cycle using a cycle-aware module and creates multiple semantic views of the cardiac cycle. It leverages a transformer to capture detailed features within each cycle and the dynamics across cycles. Our method outperforms existing approaches, exhibiting the lowest RMSE across five paired PPG-ECG databases. Additionally, extensive experiments are conducted on four cardiovascular-related tasks to assess the clinical utility of the generated ECG, achieving consistent state-of-the-art performance. Experimental results confirm that CATransformer generates highly faithful ECG signals while preserving their physiological characteristics.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A LLM-Based Hybrid-Transformer Diagnosis System in Healthcare. 基于 LLM 的混合变压器医疗诊断系统。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3481412

Dongyuan Wu, Liming Nie, Rao Asad Mumtaz, Kadambri Agarwal

{"title":"A LLM-Based Hybrid-Transformer Diagnosis System in Healthcare.","authors":"Dongyuan Wu, Liming Nie, Rao Asad Mumtaz, Kadambri Agarwal","doi":"10.1109/JBHI.2024.3481412","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3481412","url":null,"abstract":"The application of computer vision-powered large language models (LLMs) for medical image diagnosis has significantly advanced healthcare systems. Recent progress in developing symmetrical architectures has greatly impacted various medical imaging tasks. While CNNs or RNNs have demonstrated excellent performance, these architectures often face notable limitations of substantial losses in detailed information, such as requiring to capture global semantic information effectively and relying heavily on deep encoders and aggressive downsampling. This paper introduces a novel LLM-based Hybrid-Transformer Network (HybridTransNet) designed to encode tokenized Big Data patches with the transformer mechanism, which elegantly embeds multimodal data of varying sizes as token sequence inputs of LLMS. Subsequently, the network performs both inter-scale and intra-scale self-attention, processing data features through a transformer-based symmetric architecture with a refining module, which facilitates accurately recovering both local and global context information. Additionally, the output is refined using a novel fuzzy selector. Compared to other existing methods on two distinct datasets, the experimental findings and formal assessment demonstrate that our LLM-based HybridTransNet provides superior performance for brain tumor diagnosis in healthcare informatics.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest 使用神经随机森林为医疗监控系统识别面部表情。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3482450

Muhammad Hameed Siddiqi;Irshad Ahmad;Yousef Alhwaiti;Faheem Khan

{"title":"Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest","authors":"Muhammad Hameed Siddiqi;Irshad Ahmad;Yousef Alhwaiti;Faheem Khan","doi":"10.1109/JBHI.2024.3482450","DOIUrl":"10.1109/JBHI.2024.3482450","url":null,"abstract":"Facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficulty in capturing subtle features. This research introduced an ensemble neural random forest method that utilizes convolutional neural network (CNN) architecture for feature extraction and optimized random forest for classification. For feature extraction, four convolutional layers with different numbers of filters and kernel sizes are used. Further, the maxpooling, batch normalization, and dropout layers are used in the model to expedite the process of feature extraction and avoid the overfitting of the model. The extracted features are provided to the optimized random forest for classification, which is based on the number of trees, criterion, maximum tree depth, maximum terminal nodes, minimum sample split, and maximum features per tree, and applied to the classification process. To demonstrate the significance of the proposed model, we conducted a thorough assessment of the proposed neural random forest through an extensive experiment encompassing six publicly available datasets. The remarkable weighted average recognition rate of 97.3% achieved across these diverse datasets highlights the effectiveness of our approach in the context of FER systems.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"30-42"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prior Visual-guided Self-supervised Learning Enables Color Vignetting Correction for High-throughput Microscopic Imaging. 先验视觉引导的自我监督学习实现了高通量显微成像的色彩晕影校正。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3471907

Jianhang Wang, Tianyu Ma, Luhong Jin, Yunqi Zhu, Jiahui Yu, Feng Chen, Shujun Fu, Yingke Xu

{"title":"Prior Visual-guided Self-supervised Learning Enables Color Vignetting Correction for High-throughput Microscopic Imaging.","authors":"Jianhang Wang, Tianyu Ma, Luhong Jin, Yunqi Zhu, Jiahui Yu, Feng Chen, Shujun Fu, Yingke Xu","doi":"10.1109/JBHI.2024.3471907","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3471907","url":null,"abstract":"Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent at present. In this paper, we take advantage of a prior knowledge about the homogeneity of microscopic images and radial attenuation property of vignetting to develop a self-supervised deep learning algorithm that achieves complex vignetting removal in color microscopic images. Our proposed method, vignetting correction lookup table (VCLUT), is trainable on both single and multiple images, which employs adversarial learning to effectively transfer good imaging conditions from the user visually defined central region of its own light field to the entire image. To illustrate its effectiveness, we performed individual correction experiments on data from five distinct biological specimens. The results demonstrate that VCLUT exhibits enhanced performance compared to classical methods. We further examined its performance as a multi-image-based approach on a pathological dataset, revealing its advantage over other stateof-the-art approaches in both qualitative and quantitative measurements. Moreover, it uniquely possesses the capacity for generalization across various levels of vignetting intensity and an ultra-fast model computation capability, rendering it well-suited for integration into high-throughput imaging pipelines of digital microscopy.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks mDARTS：针对成员推断攻击搜索基于 ML 的心电图分类器。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3481505

Eunbin Park;Youngjoo Lee

{"title":"mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks","authors":"Eunbin Park;Youngjoo Lee","doi":"10.1109/JBHI.2024.3481505","DOIUrl":"10.1109/JBHI.2024.3481505","url":null,"abstract":"This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that balance high classification performance with robust privacy protection against membership inference attacks (MIA). We introduce a comprehensive approach that innovates in both machine learning efficacy and privacy preservation. Key contributions include the development of a privacy estimator to quantify and mitigate privacy leakage in neural network architectures used for ECG classification. Utilizing this privacy estimator, we propose mDARTS (searching ML-based ECG classifier against MIA), integrating MIA's attack loss into the architecture search process to identify architectures that are both accurate and resilient to MIA threats. Our method achieves significant improvements, with an ECG classification accuracy of 92.1% and a lower privacy score of 54.3%, indicating reduced potential for sensitive information leakage. Heuristic experiments refine architecture search parameters specifically for ECG classification, enhancing classifier performance and privacy scores by up to 3.0% and 1.0%, respectively. The framework's adaptability supports user customization, enabling the extraction of architectures that meet specific criteria such as optimal classification performance with minimal privacy risk. By focusing on the intersection of high-performance ECG classification and the mitigation of privacy risks associated with MIA, our study offers a pioneering solution addressing the limitations of previous approaches.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"177-187"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI 利用纵向 sMRI 通过病变特征选择进行早期阿尔茨海默病预测的注意力引导 3D CNN。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-16 DOI: 10.1109/JBHI.2024.3482001

Jinwei Liu;Yashu Xu;Yi Liu;Huating Luo;Wenxiang Huang;Lizhong Yao

{"title":"Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI","authors":"Jinwei Liu;Yashu Xu;Yi Liu;Huating Luo;Wenxiang Huang;Lizhong Yao","doi":"10.1109/JBHI.2024.3482001","DOIUrl":"10.1109/JBHI.2024.3482001","url":null,"abstract":"Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"324-332"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction 时空神经网络的可解释多分支架构及其在癫痫发作预测中的应用

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481005

Baolian Shan;Haiqing Yu;Yongzhi Huang;Minpeng Xu;Dong Ming

{"title":"Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction","authors":"Baolian Shan;Haiqing Yu;Yongzhi Huang;Minpeng Xu;Dong Ming","doi":"10.1109/JBHI.2024.3481005","DOIUrl":"10.1109/JBHI.2024.3481005","url":null,"abstract":"Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions. Therefore, it may be underperforming and uninterpretable for the CNNs without capturing complex spectral properties sufficiently. This study proposed a novel interpretable multi-branch architecture for spatiotemporal CNNs, namely MultiSincNet. On the one hand, the MultiSincNet could directly show the frequency boundaries using the interpretable sinc-convolution layers. On the other hand, it could extract and integrate multiple spatiotemporal features across varying spectral resolutions using parallel branches. Moreover, we also constructed a post-hoc explanation technique for multi-branch CNNs, using the first- order Taylor expansion and chain rule based on the multivariate composite function, which demonstrates the crucial spatiotemporal features learned by the proposed multi-branch spatiotemporal CNN. When combined with the optimal MultiSincNet, ShallowConvNet, DeepConvNet, and EEGWaveNet had significantly improved the subject-specific performance on most metrics. Specifically, the optimal MultiSincNet significantly increased the average accuracy, sensitivity, specificity, binary F1-score, weighted F1-score, and AUC of EEGWaveNet by about 7%, 8%, 7%, 8%, 7%, and 7%, respectively. Besides, the visualization results showed that the optimal model mainly extracts the spectral energy difference from the high gamma band focalized to specific spatial areas as the dominant spatiotemporal EEG feature.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"235-247"},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch 基于联合能量的半监督呼吸声分类模型：一种对分布不匹配不敏感的方法

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3480999

Wenjie Song;Jiqing Han;Shiwen Deng;Tieran Zheng;Guibin Zheng;Yongjun He

{"title":"Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch","authors":"Wenjie Song;Jiqing Han;Shiwen Deng;Tieran Zheng;Guibin Zheng;Yongjun He","doi":"10.1109/JBHI.2024.3480999","DOIUrl":"10.1109/JBHI.2024.3480999","url":null,"abstract":"Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respiratory sound classification, in practice, it usually takes years to acquire a sufficiently sizeable unlabeled set, which consequently results in an extension of the research timeline. Considering that there are also respiratory sounds available in other related tasks, like breath phase detection and COVID-19 detection, it might be an alternative manner to treat these external samples as unlabeled data for respiratory sound classification. However, since these external samples are collected in different scenarios via different devices, there inevitably exists a distribution mismatch between the labeled and external unlabeled data. For existing methods, they usually assume that the labeled and unlabeled data follow the same data distribution. Therefore, they cannot benefit from external samples. To utilize external unlabeled data, we propose a semi-supervised method based on Joint Energy-based Model (JEM) in this paper. During training, the method attempts to use only the essential semantic components within the samples to model the data distribution. When non-semantic components like recording environments and devices vary, as these non-semantic components have a small impact on the model training, a relatively accurate distribution estimation is obtained. Therefore, the method exhibits insensitivity to the distribution mismatch, enabling the model to leverage external unlabeled data to mitigate the lack of labeled data. Taking ICBHI 2017 as the labeled set, HF_Lung_V1 and COVID-19 Sounds as the external unlabeled sets, the proposed method exceeds the baseline by 12.86.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 2","pages":"1433-1443"},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fall Detection Method Based on a Human Electrostatic Field and VMD-ECANet Architecture 基于人体静电场和 VMD-ECANet 架构的跌倒检测方法。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-15 DOI: 10.1109/JBHI.2024.3481237

Xi Chen;Jiaao Yan;Sichao Qin;Pengfei Li;Shuangqian Ning;Yuting Liu

{"title":"Fall Detection Method Based on a Human Electrostatic Field and VMD-ECANet Architecture","authors":"Xi Chen;Jiaao Yan;Sichao Qin;Pengfei Li;Shuangqian Ning;Yuting Liu","doi":"10.1109/JBHI.2024.3481237","DOIUrl":"10.1109/JBHI.2024.3481237","url":null,"abstract":"Falls are one of the most serious health risks faced by older adults worldwide, and they can have a significant impact on their physical and mental well-being as well as their quality of life. Detecting falls promptly and accurately and providing assistance can effectively reduce the harm caused by falls to older adults. This paper proposed a noncontact fall detection method based on the human electrostatic field and a VMD-ECANet framework. An electrostatic measurement system was used to measure the electrostatic signals of four types of falling postures and five types of daily actions. The signals were randomly divided in proportion and by individuals to construct a training set and test set. A fall detection model based on the VMD-ECA network was proposed that decomposes electrostatic signals into modal component signals using the variational mode decomposition (VMD) technique. These signals were then fed into a multichannel convolutional neural network for feature extraction. Information fusion was achieved through the efficient channel attention network (ECANet) module. Finally, the extracted features were input into a classifier to obtain the output results. The constructed model achieved an accuracy of 96.44%. The proposed fall detection solution has several advantages, including being noncontact, cost-effective, and privacy friendly. It is suitable for detecting indoor falls by older individuals living alone and helps to reduce the harm caused by falls.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"583-595"},"PeriodicalIF":6.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning TKR-FSOD：利用拓扑知识推理的胎儿解剖结构少拍检测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-14 DOI: 10.1109/JBHI.2024.3480197

Xi Li;Ying Tan;Bocheng Liang;Bin Pu;Jiewen Yang;Lei Zhao;Yanqing Kong;Lixian Yang;Rentie Zhang;Hao Li;Shengli Li

{"title":"TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning","authors":"Xi Li;Ying Tan;Bocheng Liang;Bin Pu;Jiewen Yang;Lei Zhao;Yanqing Kong;Lixian Yang;Rentie Zhang;Hao Li;Shengli Li","doi":"10.1109/JBHI.2024.3480197","DOIUrl":"10.1109/JBHI.2024.3480197","url":null,"abstract":"Fetal multi-anatomical structure detection in ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images, but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure few-shot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second-best method with a maximum margin of 4.8% on 5-shot of split 1 under four-chamber cardiac view.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 1","pages":"547-557"},"PeriodicalIF":6.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0