IEEE Journal of Biomedical and Health Informatics最新文献_第10页

Development of a tongue image-based machine learning tool for the diagnosis of colorectal cancer: a prospective multicentre clinical cohort study. 基于舌头图像的结直肠癌诊断机器学习工具的开发：一项前瞻性多中心临床队列研究。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3585552

Xiaohe Sun, Letian Huang, Libo Qu, Cheng Chen, Xing Zeng, Zuojian Zhou, Hongyan Li, Jin Sun, Xufeng Lang, Jie Guo, Haibo Cheng

{"title":"Development of a tongue image-based machine learning tool for the diagnosis of colorectal cancer: a prospective multicentre clinical cohort study.","authors":"Xiaohe Sun, Letian Huang, Libo Qu, Cheng Chen, Xing Zeng, Zuojian Zhou, Hongyan Li, Jin Sun, Xufeng Lang, Jie Guo, Haibo Cheng","doi":"10.1109/JBHI.2025.3585552","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3585552","url":null,"abstract":"Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptimal patient compliance rates. This study develops an intelligent diagnostic model based on tongue images to assist in CRC diagnosis, leveraging the integrative potential of traditional tongue diagnosis and modern machine learning. Between June 2023 and July 2024, we collected and processed 1,389 tongue images from CRC patients and 1,543 from non-colorectal cancer (NCRC) participants. Our methodology combines innovative image segmentation using the Segment Anything Model (SAM) with Grounding DINO, extracts both hand-crafted features (color, texture, shape) and deep learning features via Swin-Transformer, and employs feature fusion and selection techniques. The diagnostic model achieves an accuracy of 87.93% (F1-score: 0.9072) in internal validation. In an independent external cohort of 119 CRC patients and 221 NCRC participants, it demonstrates 85.18% precision (recall: 85%, F1-score: 0.8507). This noninvasive, cost-effective approach demonstrates significant potential as a complementary screening tool for CRC, particularly in regions with limited access to conventional diagnostic resources.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553406","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

Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images. 利用视觉语言嵌入在组织病理学图像中的零射击学习。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3584802

Md Mamunur Rahaman, Ewan K A Millar, Erik Meijering

{"title":"Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images.","authors":"Md Mamunur Rahaman, Ewan K A Millar, Erik Meijering","doi":"10.1109/JBHI.2025.3584802","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584802","url":null,"abstract":"Zero-shot learning (ZSL) offers tremendous potential for histopathology image analysis, enabling models to generalize to unseen classes without extensive labeled data. Recent vision-language model (VLM) advancements have expanded ZSL capabilities, allowing task performance without task-specific fine-tuning. However, applying VLMs to histopathology presents considerable challenges due to the complexity of histopathological imagery and the nuanced nature of diagnostic tasks. We propose Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE), a novel framework for zero-shot histopathology image classification. MR-PHE mimics pathologists' workflow through multiresolution patch extraction to capture key cellular and tissue features. It introduces a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings, effectively combining local and global contextual information. Additionally, we develop a comprehensive prompt generation and selection framework, enriching class descriptions with domain-specific synonyms and clinically relevant features to enhance semantic understanding. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings, emphasizing diagnostically important regions during classification. Experimental results demonstrate MR-PHE significantly improves zero-shot classification performance on histopathology datasets, often surpassing fully supervised models, showing its effectiveness and potential to advance computational pathology.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553408","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

LLM-DDI: Leveraging Large Language Models for Drug-Drug Interaction Prediction on Biomedical Knowledge Graph. LLM-DDI：利用大语言模型在生物医学知识图谱上进行药物-药物相互作用预测。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3585290

Dongxu Li, Yue Yang, Ziwen Cui, Hengchuang Yin, Pengwei Hu, Lun Hu

{"title":"LLM-DDI: Leveraging Large Language Models for Drug-Drug Interaction Prediction on Biomedical Knowledge Graph.","authors":"Dongxu Li, Yue Yang, Ziwen Cui, Hengchuang Yin, Pengwei Hu, Lun Hu","doi":"10.1109/JBHI.2025.3585290","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3585290","url":null,"abstract":"Drug-drug interaction (DDI) refers to the interaction relationships between drugs. Discovering new DDIs is crucial for advancing drug development and enhancing clinical treatments. Given the significant progress achieved through graph neural networks (GNNs), network-based models have become a prevalent approach for tackling this challenge. However, current network-based approaches are incapable of seamlessly integrating a wide range of information. Motivated by this discovery, we propose a novel model, namely LLM-DDI, which aims to comprehensively tackle DDI prediction tasks by integrating various information of molecules in the BKG. LLM-DDI initially incorporates the generative pre-trained transformer (GPT) model to generate embeddings for each molecule within the biomedical knowledge graph (BKG). These embeddings encompass diverse types of information pertaining to each molecule. Subsequently, LLM-DDI utilizes a message-passing GNN framework to enhance the learning of molecular representations with the embeddings derived from GPT as input. LLM-DDI governs the propagation of information within the BKG by semantic relationships. These semantic relationships determine how information flows and is exchanged between different entities in the BKG. Finally, LLM-DDI leverages the learned drug representations to predict potential DDIs. Experiments show the effectiveness of LLM-DDI, as it achieves the best performance on two real-world datasets, providing valuable guidance for drug development and clinical treatment.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553409","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

An EEG-based seizure prediction model encoding brain network temporal dynamics. 基于脑电图的癫痫发作预测模型，编码脑网络时间动态。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3584861

Jiahui Liao, Yiyi Chen, Yihang He, Kai Zhang, Ting Ma, Yilong Wang, Xiaoqiu Shao

{"title":"An EEG-based seizure prediction model encoding brain network temporal dynamics.","authors":"Jiahui Liao, Yiyi Chen, Yihang He, Kai Zhang, Ting Ma, Yilong Wang, Xiaoqiu Shao","doi":"10.1109/JBHI.2025.3584861","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584861","url":null,"abstract":"EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the temporal dynamics of epileptic brain networks. Metastability, which describes recurring topographical patterns of spontaneous neural activity over time, provides a unique perspective for capturing the dynamic evolution before seizure onset. In this study, we propose a seizure prediction model that fuses consistent epileptic network processes across subjects into a higher-order latent space. Specifically, we first construct metastable transition patterns to identify the recurrent network states over time. Through adversarial feature learning, we then impose the metastability prior on the latent embedding space encoded via a variational autoencoder (VAE), while leveraging the maximum mean discrepancy measure (MMD) to further mitigate the patient gap. The latent representation, endowed with physiological priors, is ultimately utilized for patient-independent seizure prediction. We evaluate our method on two publicly available and one clinical scalp EEG datasets. Compared to the existing methods, our method has improved AUC, sensitivity, and specificity on CHB-MIT dataset by approximately 9%, 5%, and 5%, respectively. Our method shows that combining brain network-based physiological prior with deep learning for EEG representation learning is a brand-new strategy for associating seizures with complex brain network variations, enabling reliable patientindependent seizure prediction.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553386","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

An Autonomous AI Framework for Knee Osteoarthritis Diagnosis via Semi-Supervised Learning and Dual Knowledge Distillation. 基于半监督学习和双知识蒸馏的膝关节骨关节炎自主AI诊断框架。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3585557

Li Peng, Li Xu, Xiaoding Wang, Lizhao Wu, Jin Liu, Weiquan Zeng, Md Jalil Piran

{"title":"An Autonomous AI Framework for Knee Osteoarthritis Diagnosis via Semi-Supervised Learning and Dual Knowledge Distillation.","authors":"Li Peng, Li Xu, Xiaoding Wang, Lizhao Wu, Jin Liu, Weiquan Zeng, Md Jalil Piran","doi":"10.1109/JBHI.2025.3585557","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3585557","url":null,"abstract":"In the diagnosis of knee osteoarthritis, imaging analysis relies on accurate classification models to assess the severity of the disease. Traditional methods often require large amounts of labeled data, which is challenging in many developing countries, especially in resource-limited areas where the scarcity of labeled data becomes a bottleneck due to a lack of medical resources and qualified annotators. Privacy concerns also arise when using high-quality datasets from developed countries. This paper proposes a semi-supervised dual-knowledge distillation framework, PADistillation, that leverages autonomous AI to expand the reach of telemedicine and remote diagnostics while addressing data scarcity and privacy problems. To overcome the challenge of insufficient labeled data, the framework uses attention-guided distillation, employing high-attention pixels and channels to guide the student model's learning, thereby enhancing classification performance with limited labeled data. To ensure patient privacy during training, a personalized pixel shuffling method is proposed, dynamically determining the privacy protection priority of different regions by measuring the visual disorder of image areas. Through autonomous optimization and real-time decision making, PADistillation operates efficiently in resourceconstrained environments and supports telemedicine and remote diagnostic needs. Even with limited labeled data, the experimental results show that PADistillation achieves an accuracy rate of 88.19%, a precision rate of 86.28%, and an F1 score of 86.94%. Compared with the mainstream semi-supervised methods, its accuracy rate is increased by more than 2%, the training efficiency is improved by 30%, and the privacy protection mechanism only leads to a performance loss of 1.2%.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553385","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

Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features. 用于早期阿尔茨海默病检测的混合变压器：基于手写的二维图像和一维信号特征的集成。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3585379

Changqing Gong, Huafeng Qin, Mounim A El-Yacoubi

{"title":"Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features.","authors":"Changqing Gong, Huafeng Qin, Mounim A El-Yacoubi","doi":"10.1109/JBHI.2025.3585379","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3585379","url":null,"abstract":"Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a non-invasive and cost-effective way to capture subtle motor changes. State-of-the-art research on handwriting, mostly online, based AD detection has predominantly relied on manually extracted features, fed as input to shallow machine learning models. Some recent works have proposed deep learning (DL)-based models, either 1D-CNN or 2D-CNN architectures, with performance comparing favorably to handcrafted schemes. These approaches, however, overlook the intrinsic relationship between the 2D spatial patterns of handwriting strokes and their 1D dynamic characteristics, thus limiting their capacity to capture the multimodal nature of handwriting data. Moreover, the application of Transformer models remains basically unexplored. To address these limitations, we propose a novel approach for AD detection, consisting of a learnable multimodal hybrid attention model that integrates simultaneously 2D handwriting images with 1D dynamic handwriting signals. Our model leverages a gated mechanism to combine similarity and difference attention, blending the two modalities and learning robust features by incorporating information at different scales. Our model achieved state-of-the-art performance on the DARWIN dataset, with an F1-score of 90.32% and accuracy of 90.91% in Task 8 ('L' writing), surpassing the previous best by 4.61% and 6.06% respectively.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553407","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

Robust Multi-contrast MRI Medical Image Translation via Knowledge Distillation and Adversarial Attack. 基于知识蒸馏和对抗攻击的鲁棒多对比MRI医学图像翻译。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-02 DOI: 10.1109/JBHI.2025.3584721

Xujie Zhao, Feng Liang, Chengjiang Long, Zhiyong Yuan, Jianhui Zhao

{"title":"Robust Multi-contrast MRI Medical Image Translation via Knowledge Distillation and Adversarial Attack.","authors":"Xujie Zhao, Feng Liang, Chengjiang Long, Zhiyong Yuan, Jianhui Zhao","doi":"10.1109/JBHI.2025.3584721","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584721","url":null,"abstract":"Medical image translation is of great value but is very difficult due to the requirement with style change of noise pattern and anatomy invariance of image content. Various deep learning methods like the mainstream GAN, Transformer and Diffusion models have been developed to learn the multi-modal mapping to obtain the translated images, but the results from the generator are still far from being perfect for medical images. In this paper, we propose a robust multi-contrast translation framework for MRI medical images with knowledge distillation and adversarial attack, which can be integrated with any generator. The additional refinement network consists of teacher and student modules with similar structures but different inputs. Unlike the existing knowledge distillation works, our teacher module is designed as a registration network with more inputs to better learn the noise distribution well and further refine the translated results in the training stage. The knowledge is then well distilled to the student module to ensure that better translation results are generated. We also introduce an adversarial attack module before the generator. Such a black-box attacker can generate meaningful perturbations and adversarial examples throughout the training process. Our model has been tested on two public MRI medical image datasets considering different types and levels of perturbations, and each designed module is verified by the ablation study. The extensive experiments and comparison with SOTA methods have strongly demonstrated our model's superiority of refinement and robustness.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553410","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

High-resolution and wearable magnetocardiography (MCG) measurement with active-passive coupling magnetic control method. 采用主-被动耦合磁控制方法的高分辨率可穿戴心脏磁图测量。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-07-01 DOI: 10.1109/JBHI.2025.3584984

Shuai Dou, Xikai Liu, Pengfei Song, Yidi Cao, Tong Wen, Rui Feng, Bangcheng Han

{"title":"High-resolution and wearable magnetocardiography (MCG) measurement with active-passive coupling magnetic control method.","authors":"Shuai Dou, Xikai Liu, Pengfei Song, Yidi Cao, Tong Wen, Rui Feng, Bangcheng Han","doi":"10.1109/JBHI.2025.3584984","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584984","url":null,"abstract":"Magnetocardiography (MCG) enables passive detection of weak magnetic fields generated by the heart with high sensitivity, which can offer valuable information for diagnosing and treating heart conditions. Due to the limitations of the geomagnetic field and unknown magnetic interference, the MCG signals are often overwhelmed by high levels of magnetic noise. In this paper, we propose the design of a high-resolution and movable MCG system comprised of an active-passive coupling magnetic control (AP-CMC) system and a wearable multi-channel signal detection array. The system realizes the MCG measurement at the same time as the AP-CMC system eliminates interference in real time, i.e., simultaneous control and simultaneous measurement. Dynamic MCG signal measurements were successfully conducted, obtaining typical characteristic features of MCG signals. Our method shows promise in enhancing the accuracy and expanding the scope of MCG measurement applications, thereby offering valuable insights for the early diagnosis and precise localization of heart diseases.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540054","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

Optimizing Patient Feedback with Generative Adversarial Network Leveraging Knowledge Distillation to Improve Healthcare. 利用知识蒸馏生成对抗网络优化患者反馈以改善医疗保健。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-30 DOI: 10.1109/JBHI.2025.3584240

Md Fahim-Ul-Islam, Amitabha Chakrabarty, Mehedi Hasan, Abrar Hasan Efaz, Xiaoding Wang, Md Jalil Piran

{"title":"Optimizing Patient Feedback with Generative Adversarial Network Leveraging Knowledge Distillation to Improve Healthcare.","authors":"Md Fahim-Ul-Islam, Amitabha Chakrabarty, Mehedi Hasan, Abrar Hasan Efaz, Xiaoding Wang, Md Jalil Piran","doi":"10.1109/JBHI.2025.3584240","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584240","url":null,"abstract":"Despite progress in global healthcare systems, the utilization of domestic healthcare facilities remains limited in several regions, with a considerable proportion of people pursuing treatment overseas. This development highlights the necessity for systematic incorporation of patient-centered input, an essential element for enhancing accountability, transparency, and quality in local healthcare. Our research seeks to address this deficiency by establishing a system that collects and analyzes patient feedback to inform and improve healthcare policies and practices, particularly in areas with elevated demand for medical services. We offer an effective platform for viewing reviews from different hospitals, especially in places where people routinely visit for medical services. Therefore, we built our primary \"Dhaka Private Hospitals Review Dataset,\" considering gathering and evaluating patient opinions methodically. We further employ transformer-based generative adversarial learning to evaluate sentiment analysis using knowledge distillation (KD) to boost model efficiency. Our proposed GANBERT architecture includes two optimized student models gated recurrent unit-based Contextualized BERT (GC-BERT) and LSTM-based Contextualized BERT (LC-BERT) with enhanced generators and discriminators. Our GC-BERT enhances execution time by 1.27% to 24.27%, while LC-BERT improves by 14.13% to 23.30%, showing superior advancements compared to other contemporary models. Each model with reductions ranging from 82.50% to 99.99% parameters, making them lightweight and efficient compared to other teacher models in the KD process. Instead of using contextual word representations which demand more space and complexity for reviewing patient feedback, we utilize the single static pretrained and low-dimensional word embedding space approach integrating student models.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527664","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

Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation. 半监督胎儿超声图像分割的双向原型引导一致性约束。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-06-30 DOI: 10.1109/JBHI.2025.3584236

Chongwen Lyu, Kai Han, Lu Liu, Jun Chen, Lele Ma, Zheng Pang, Zhe Liu

{"title":"Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation.","authors":"Chongwen Lyu, Kai Han, Lu Liu, Jun Chen, Lele Ma, Zheng Pang, Zhe Liu","doi":"10.1109/JBHI.2025.3584236","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3584236","url":null,"abstract":"Fetal ultrasound (US) image segmentation plays an important role in fetal development assessment, maternal pregnancy management, and intrauterine surgery planning. However, obtaining large-scale, accurately annotated fetal US imaging data is time-consuming and labor-intensive, posing challenges to the application of deep learning in this field. To address this challenge, we propose a semi-supervised fetal US image segmentation method based on bidirectional prototype-guided consistency constraint (BiPCC). BiPCC utilizes the prototype to bridge labeled and unlabeled data and establishes interaction between them. Specifically, the model generates pseudo-labels using prototypes from labeled data and then utilizes these pseudo-labels to generate pseudo-prototypes for segmenting the labeled data inversely, thereby achieving bidirectional consistency. Additionally, uncertainty-based cross-supervision is incorporated to provide additional supervision signals, thereby enhancing the quality of pseudo-labels. Extensive experiments on two fetal US datasets demonstrate that BiPCC outperforms state-of-the-art methods for semi-supervised fetal US segmentation. Furthermore, experimental results on two additional medical segmentation datasets exhibit BiPCC's outstanding generalization capability for diverse medical image segmentation tasks. Our proposed method offers a novel insight for semi-supervised fetal US image segmentation and holds promise for further advancing the development of intelligent healthcare.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144527706","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