IEEE Journal of Biomedical and Health Informatics最新文献_第2页

VLD-Net: Localization and Detection of the Vertebrae from X-ray Images by Reinforcement Learning with Adaptive Exploration Mechanism and Spine Anatomy Information. VLD-Net：通过自适应探索机制和脊柱解剖信息的强化学习，从 X 射线图像中定位和检测椎骨。

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3553935

Shun Xiang, Lei Zhang, Yuanquan Wang, Shoujun Zhou, Xing Zhao, Tao Zhang, Shuo Li

{"title":"VLD-Net: Localization and Detection of the Vertebrae from X-ray Images by Reinforcement Learning with Adaptive Exploration Mechanism and Spine Anatomy Information.","authors":"Shun Xiang, Lei Zhang, Yuanquan Wang, Shoujun Zhou, Xing Zhao, Tao Zhang, Shuo Li","doi":"10.1109/JBHI.2025.3553935","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553935","url":null,"abstract":"Accurate and efficient vertebrae localization and detection in X-ray images are essential for diagnosing and treating spinal diseases. However, most existing methods struggle with the complexity of spine X-ray images, yielding inaccurate results due to insufficient utilization of spinal anatomy information and neglect of individual vertebra characteristics. In this paper, we propose an innovative Vertebrae Localization and Detection Network (VLD-Net) to accurately assist physicians in diagnosing spine-related diseases from X-ray images. Our VLD-Net, for the first time, defines vertebrae localization as a top-bottom sequential decision-making process, employing deep reinforcement learning (DRL) to fully leverage the anatomical information of the spine. Simultaneously, it also prioritizes the distinct characteristics of each vertebra for accurate detection. Specifically, VLD-Net combines three key components: (1) An advanced vertebrae localization module based on DRL is proposed, effectively leveraging anatomical information of the spine. (2) A novel adaptive exploration mechanism is coined to understand the behavior of the DRL agent during training, pinpointing how to effectively achieve the trade-off between exploration and exploitation. (3) An innovative vertebra-focused module is proposed to accurately detect vertebral landmarks, using the attention region of each vertebra as input to enhance focus on the target and reduce interference from surrounding tissue. Extensive experiments on two public spine datasets demonstrate that the VLD-Net outperforms the state-of-the-art methods in accuracy and robustness. Our code is available at https://github.com/hlyf-xs/VLD-Net.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729924","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

Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning. 利用子图匹配学习揭示蛋白质网络中的草药症状关联和作用机制

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-26 DOI: 10.1109/JBHI.2025.3554520

Menglu Li, Yongkang Wang, Yujing Ni, Hui Xiong, Zhinan Mei, Wen Zhang

{"title":"Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning.","authors":"Menglu Li, Yongkang Wang, Yujing Ni, Hui Xiong, Zhinan Mei, Wen Zhang","doi":"10.1109/JBHI.2025.3554520","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554520","url":null,"abstract":"In traditional Chinese medicine, deciphering herb-symptom associations (HSAs) and revealing their mechanisms of action are crucial for bridging traditional knowledge and modern biomedicine. While previous studies have investigated HSAs using protein-protein interaction (PPI)-based network medicine method, they often treat all proteins equally, failing to capture the heterogeneous contributions of individual proteins to HSAs. This limitation hinders their capacity to reveal the mechanisms of action. To address this challenge, we propose a subgraph matching learning method, GraphHSA, for HSA prediction. GraphHSA maps herbs and symptoms onto the PPI network to construct subgraphs. Then, GraphHSA utilizes an attention mechanism to compute the importance of each protein on the subgraph, and weighted aggregate protein information to generate herb/symptom embeddings. Subsequently, these embeddings are combined to model the matching relationship between herb and symptom subgraphs, enabling association prediction. Additionally, a dual-contrastive learning strategy is introduced to generate discriminative representations to enhance prediction. Experiments indicate that GraphHSA not only applies to individual herbs but also extends to compound formulations composed of multiple herbs. By capturing the dynamic interactions among their components, GraphHSA enables the identification of key biological targets and the elucidation of the mechanisms underlying their therapeutic efficacy.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729921","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

PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-25 DOI: 10.1109/JBHI.2025.3554495

Renju Liu, Jianfei Shen, Yang Gu, Yiqiang Chen, Jiling Zhang, Qingyu Wu, Chenyang Xu, Feiyi Fan

{"title":"PhysCL: Knowledge-Aware Contrastive Learning of Physiological Signal Models for Cuff-Less Blood Pressure Estimation.","authors":"Renju Liu, Jianfei Shen, Yang Gu, Yiqiang Chen, Jiling Zhang, Qingyu Wu, Chenyang Xu, Feiyi Fan","doi":"10.1109/JBHI.2025.3554495","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3554495","url":null,"abstract":"Training deep learning models for photoplethysmography(PPG)-based cuff-less blood pressure estimation often requires a substantial amount of labeled data collected through sophisticated medical instruments, posing significant challenges in practical applications. To address this issue, we propose Physiological Knowledge-Aware Contrastive Learning (PhysCL), a novel approach designed to reduce the dependence on labeled PPG data while improving blood pressure estimation accuracy. Specifically, PhysCL tackles the semantic consistency problem in contrastive learning by introducing a knowledge-aware augmentation bank, which generates positive physiological signal pairs using knowledge-based constraints during the contrastive pair generation. Additionally, we propose a contrastive feature reconstruction method to enhance feature diversity and prevent model collapse through feature re-sampling and re-weighting. We evaluate PhysCL on data from 106 subjects across the MIMIC III, MIMIC IV, and UQVS datasets under cross-dataset validation settings, comparing it against state-of-the-art contrastive learning methods and blood pressure estimation models. PhysCL achieves an average mean absolute error of 9.5/5.9 mmHg (systolic/diastolic) across the three datasets, using only 2% labeled data combined with 98% unlabeled data for pre-training and 5 samples for personalization, which represents a 6.2%/4.3% improvement, respectively, over the current best supervised methods. The ablation study provides further convincing evidence that the unlabeled data can be utilized to improve the existing cuff-less blood pressure estimation models and shed light on unsupervised contrastive learning for physiological signals.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708152","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

Cortico-ocular Coupling Analysis for Developmental and Behavioral Disorders: A Review.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-25 DOI: 10.1109/JBHI.2025.3553864

Hanlin Zhang, Zhiyong Wang, Chunchun Hu, Peilian Chi, Xiu Xu, Honghai Liu

{"title":"Cortico-ocular Coupling Analysis for Developmental and Behavioral Disorders: A Review.","authors":"Hanlin Zhang, Zhiyong Wang, Chunchun Hu, Peilian Chi, Xiu Xu, Honghai Liu","doi":"10.1109/JBHI.2025.3553864","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553864","url":null,"abstract":"Developmental and behavioral disorders (DBD) have a significant impact on children's neurological activity and behavioral performance. Early diagnosis and treatment are known to be beneficial for improving DBD outcomes, yet existing unimodal neurophysiological assessment tools for DBD yield significant heterogeneity in results, highlighting the urgent need for exploring novel assessment tools. Cortico-ocular coupling (COC) refers to the information interaction between the cerebral cortex and eyes, and COC analysis is a technique for quantitatively measuring the correlation of neural oscillations and eye movements as biomarkers for assessment and mechanism disclosure. This review focuses on COC analysis for DBD from four perspectives: neural substrates, research paradigms, analysis methods, and applications. First, this review provides a comprehensive overview of the neural substrates and evocation paradigms related to COC analysis, aiming at helping target brain region selection, experimental result analysis, and paradigm design. The neural substrates and evocation paradigms are categorized according to functional domains, including social functioning, attention, cognition, early visual processing, and motor function. Then, this review summarizes the EEG and eye-tracking features, the analysis methods, and the validation datasets involved in COC analysis, aiming at helping implement COC analysis. Next, this review presents the applications of COC analysis in DBD, proving the validity and advance of COC analysis. In the end, the limitations, challenges, and future directions of COC analysis are discussed.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709790","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

FBCPM: A Filter Bank Connectome-Based Prediction Modeling Framework for EEG Signals.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3551385

Linze Qian, Sujie Wang, Ioannis Kakkos, Xiaoyu Li, Xinyi Xu, Mengru Xu, George K Matsopoulos, Yi Sun, Jianhua Li, Chuantao Li, Yu Sun

{"title":"FBCPM: A Filter Bank Connectome-Based Prediction Modeling Framework for EEG Signals.","authors":"Linze Qian, Sujie Wang, Ioannis Kakkos, Xiaoyu Li, Xinyi Xu, Mengru Xu, George K Matsopoulos, Yi Sun, Jianhua Li, Chuantao Li, Yu Sun","doi":"10.1109/JBHI.2025.3551385","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3551385","url":null,"abstract":"The human brain connectome has long been recognized as a crucial component for various cognitive functions. While connectome-based prediction modeling (CPM) has been extensively explored for predicting behavior outcomes at the individual-level, its application to electroencephalogram (EEG) remains limited due to the inherent diversity and complexity of EEG frequency information. In the present work, we aim to address this issue by developing a filter bank CPM (FBCPM) framework that leverages narrowband EEG functional connectivity (FC) for individual prediction. Four independent datasets comprising 280 healthy subjects with 392 EEG recordings during the psychomotor vigilance test (PVT), were adopted here. Using the discovery dataset (i.e., Dataset 1) with 137 recordings, the feasibility of FBCPM was evaluated via predicting mean reaction time (RT) measures within a 15-min PVT task. The results showed that FBCPM framework achieved notable prediction accuracy and outperformed four benchmark approaches. Subsequent comprehensive internal and external validation analyses further affirmed its robustness across various hyper-parameters and generalizability to another three independent datasets (i.e., Dataset 2 to Dataset 4) with divergent recording or preprocessing settings. Moreover, the FBCPM framework exhibited satisfactory performance when generalized to time-on-task (TOT) effect measures (i.e., and ). Further investigation of contributing features to mean RT prediction indicated the remarkable predictive ability of negative features, manifesting as a pattern of low-frequency (below 8Hz) predominance and complex topological distributions. Overall, these findings indicated that FBCPM provided a significant methodological advance in EEG-based individual prediction approaches, moving a step forward towards practical application in cognitive neuroscience.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700369","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

Hepatic Vessel Roadmap Prediction Using Adaptive Tracking and Bending Energy Modeling in X-Ray Fluoroscopy.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3554189

Shuo Yang, Deqiang Xiao, Haixiao Geng, Danni Ai, Jingfan Fan, Tianyu Fu, Hong Song, Feng Duan, Jian Yang

{"title":"Hepatic Vessel Roadmap Prediction Using Adaptive Tracking and Bending Energy Modeling in X-Ray Fluoroscopy.","authors":"Shuo Yang, Deqiang Xiao, Haixiao Geng, Danni Ai, Jingfan Fan, Tianyu Fu, Hong Song, Feng Duan, Jian Yang","doi":"10.1109/JBHI.2025.3554189","DOIUrl":"10.1109/JBHI.2025.3554189","url":null,"abstract":"Dynamic visualization of the hepatic vessel is crucial in X-ray image-guided transjugular intrahepatic portosystemic shunt (TIPS) procedures. However, intraoperative breathing and the presence of guidewires complicate the prediction of the vessel position and posture without contrast agents. The respiration compensation technique aims to utilize the intraoperative respiration modeling to deform the initial vessel roadmap, thereby achieving the dynamic vessel prediction in the X-ray image sequence for the interventional guidance. Therefore, we propose a novel respiration compensation framework utilizing the adaptive tracking and bending energy modeling to achieve the stable vessel roadmap prediction under free breathing. First, we introduce the inter-frame rigid displacement compensation module based on the domain adaptation and adaptive centroid tracking. This module fits the respiratory curve from the X-ray images, providing the temporal motion priors for aligning roadmaps across frames. Second, we propose the novel deformation compensation module based on the bending energy modeling to correct the respiratory motion, wherein we utilize the energy features of the guidewires to drive the non-rigid registration. The control points sampled by the bending energy guide the local image to form the deformation field, facilitating the dynamic overlap of the deformed vessel roadmaps in X-ray images. Experimental results on simulated and clinical datasets show an average tracking error of 0.95 0.26 mm and 1.49 0.40 mm, respectively. The effective and fast (mean 57 ms per frame) compensation achieved by our framework has the potential for improving the outcome of liver intervention and reducing the reliance on contrast agents.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700371","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

MDD2DG-IRA: Multivariate Degree Distribution to Dynamic Graph With Inter-Channel Relevance Attention Mechanism for Multi-Channel Myocardial Infarction ECG Analysis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3554309

Xiaodong Yang, Guangkang Jiang, Zhengping Zhu, Dandan Wu, Aijun He, Jun Wang

{"title":"MDD2DG-IRA: Multivariate Degree Distribution to Dynamic Graph With Inter-Channel Relevance Attention Mechanism for Multi-Channel Myocardial Infarction ECG Analysis.","authors":"Xiaodong Yang, Guangkang Jiang, Zhengping Zhu, Dandan Wu, Aijun He, Jun Wang","doi":"10.1109/JBHI.2025.3554309","DOIUrl":"10.1109/JBHI.2025.3554309","url":null,"abstract":"We introduced a novel methodology Multivariate Degree Distribution to Dynamic Graph (MDD2DG) with Inter-channel Relevance Attention (IRA) mechanism to analyze multi-channel Electrocardiogram (ECG) signals and explore signal connections across different channels. Our methodology comprises three main steps. First, multi-channel cardiac signals are transformed into multi-channel visual graphs to extract crucial degree distribution features. Then, degree distributions are mapped into dynamic graphs using a neural network with an IRA mechanism. After that, critical features are extracted within dynamic graphs utilizing a Graph Convolutional Neural Networks (GCNNs), and classification is subsequently performed using a multilayer perceptron. In this model, a method of multi-scale position embedding was introduced, which significantly enhanced the processing efficiency of the model by providing a simpler yet sufficiently effective feature representation. Compared to traditional complex network methods, our approach replaces fixed formula-calculated features with dynamic graph models, resulting in improved recognition accuracy. In the experiments, we achieved an impressive 99.94% classification accuracy for distinguishing ECG signals from the five distinct locations (AMI, ASMI, ALMI, IMI and ILMI) with myocardial infarction (MI) as well as those of the healthy controls (HC). This work contributes to the analysis of complex physiological signals in the field of multi-channel ECG sequence, and provides a robust approach with promising implications for improving clinical medicine and the early detection of cardiac diseases.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700350","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

Improving Patient-ventilator Synchrony during Pressure Support Ventilation based on Reinforcement Learning Algorithm.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3551670

Liming Hao, Xiaohan Wang, Shuai Ren, Yan Shi, Maolin Cai, Tao Wang, Zujin Luo

{"title":"Improving Patient-ventilator Synchrony during Pressure Support Ventilation based on Reinforcement Learning Algorithm.","authors":"Liming Hao, Xiaohan Wang, Shuai Ren, Yan Shi, Maolin Cai, Tao Wang, Zujin Luo","doi":"10.1109/JBHI.2025.3551670","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3551670","url":null,"abstract":"Mechanical ventilation is an effective treatment for critically ill patients and those with pulmonary diseases. However, patient-ventilator asynchrony (PVA) remains a significant challenge, potentially leading to high mortality. Improving patient-ventilator synchrony poses a complex decision-making problem in clinical practice. Traditional methods rely heavily on clinicians' experience, often resulting in inefficiencies, delayed ventilator adjustments, and resource shortages. This paper proposes a novel approach using a deep reinforcement learning (RL) algorithm based on deep Q-learning (DQN) to enhance patient-ventilator synchrony during pressure support ventilation. The action space and reward function are established from clinical experience, and a pneumatic model of the mechanical ventilation system is constructed to simulate various patient conditions and types of PVAs. Clinical data are used to evaluate the RL algorithm qualitatively and quantitatively. The RL-optimized ventilation strategy reduces the proportion of breaths containing PVAs from 37.52% to 7.08%, demonstrating its effectiveness in assisting clinical decision-making, improving synchrony, and enabling intelligent ventilator control, bedside monitoring, and automatic weaning.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700348","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

Active Learning based on Temporal Difference of Gradient Flow in Thoracic Disease Diagnosis.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3554298

Jiayi Chen, Benteng Ma, Hengfei Cui, Jingfeng Zhang, Yong Xia

{"title":"Active Learning based on Temporal Difference of Gradient Flow in Thoracic Disease Diagnosis.","authors":"Jiayi Chen, Benteng Ma, Hengfei Cui, Jingfeng Zhang, Yong Xia","doi":"10.1109/JBHI.2025.3554298","DOIUrl":"10.1109/JBHI.2025.3554298","url":null,"abstract":"Given the significant advancements in thoracic disease diagnosis due to deep learning, there is a reliance on the availability of numerous annotated samples, which, however, can hardly be guaranteed due to the resource-intensive nature of medical image annotation. Active learning has been introduced to mitigate annotation costs by selecting a subset of uncertain samples for annotation and training. Existing active learning methods encounter two primary challenges: (1) overlooking the impact of samples on the dynamics of model training during data selection, and (2) suffering from high costs of data evaluation and selection. To tackle both issues, we propose a novel metric called Temporal Difference of Gradient Flow (TDGF) for data selection in active learning. Each round of active learning involves three steps: model training, data selection, and data annotation. First, we train a target model, a proxy model, and a historical proxy model on the labeled set. Second, the TDGF scores of unlabeled samples are evaluated based on the surrogate gradient flow, i.e., the TDGF w.r.t the final fully-connected layer between the proxy and historical proxy models, and top-K samples with the highest TDGF scores are selected. Third, the selected samples are annotated, and the labeled pool and unlabeled pool are updated. Comparative experiments have been conducted on two public chest radiograph datasets, i.e., ChestX-ray14 and CheXpert. Our results suggest that the proposed TDGF metric is prone to selecting hard and uncertain samples, and the use of proxy models and surrogate gradient flow substantially reduces the complexity of TDGF calculation. More importantly, the results also indicate that our TDGF-based method outperforms classical and state-of-the-art active learning methods in thoracic disease diagnosis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700365","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

Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-24 DOI: 10.1109/JBHI.2025.3553502

Zhuolong Chen, Xiaoqing Yin, Fan Yang, Xiaofan Li, Zixuan Zhao, Xueying Li, Jianghu Liu, Yubin Zhao, Cheng-Zhong Xu, Fangfang Zheng, Yong Lin

{"title":"Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test.","authors":"Zhuolong Chen, Xiaoqing Yin, Fan Yang, Xiaofan Li, Zixuan Zhao, Xueying Li, Jianghu Liu, Yubin Zhao, Cheng-Zhong Xu, Fangfang Zheng, Yong Lin","doi":"10.1109/JBHI.2025.3553502","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3553502","url":null,"abstract":"Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700367","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