Biomedical Signal Processing and Control最新文献

{"title":"Assessment of quality of electrocardiograms, seismocardiograms, and gyrocardiograms based on features derived from symmetric projection attractor reconstruction in healthy subjects","authors":"Szymon Sieciński ,&nbsp;Muhammad Tausif Irshad ,&nbsp;Md Abid Hasan ,&nbsp;Rafał Doniec ,&nbsp;Paweł Kostka ,&nbsp;Ewaryst Tkacz ,&nbsp;Marcin Grzegorzek","doi":"10.1016/j.bspc.2025.108170","DOIUrl":"10.1016/j.bspc.2025.108170","url":null,"abstract":"<div><div>Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches. The aim of this study was to evaluate the quality of electrocardiographic (ECG) signals, seismocardiographic signals (SCG), and gyrocardiograms (GCG) based on symmetric projection attractor reconstruction (SPAR) with Takens delay coordinates with fit five classifiers: random forest, gradient boosting, random forest XGB, and support vector machines (SVM) with various number of decision tree-based estimators (100–10,000) and various kernels (linear, radial base function, and polynomial), respectively. The analysis was carried out on a public dataset “Mechanocardiograms with ECG reference” containing 29 concurrent ECG, SCG, and GCG signal recordings. The highest values without SMOTE were observed for ECG signals, SVM with fourth order polynomial kernel (accuracy of 0.6897, PPV of 0.6019, sensitivity of 0.5306, and F1 score of 0.4952), and after applying SMOTE were observed for Gradient Boosting in ECG signal (200 estimators, accuracy 0.7500, PPV of 0.7747, sensitivity of 0.7500, and F2 score of 0.7747 respectively). These findings suggest that the SPAR-based approach is a promising method to accurately assess the quality of cardiovascular signals, including seismocardiograms and gyrocardiograms.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108170"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614748","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}

{"title":"Tuberculosis detection on chest X-rays using two-dimensional multiscale symbolic dynamic entropy","authors":"Andreia S. Gaudêncio ,&nbsp;Miguel Carvalho ,&nbsp;Pedro G. Vaz ,&nbsp;João M. Cardoso ,&nbsp;Anne Humeau-Heurtier","doi":"10.1016/j.bspc.2025.108346","DOIUrl":"10.1016/j.bspc.2025.108346","url":null,"abstract":"<div><div>Several radiological patterns associated with pulmonary tuberculosis (TB) have been identified on chest X-rays (CXR) used for screening purposes. As a result, several automatic computational tools have emerged for this purpose. We propose a new algorithm, two-dimensional multiscale symbolic dynamic entropy (MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span>), to develop a computational tool sensitive to these subtle patterns variations and noise robustness for evaluating CXR images from healthy and TB-diagnosed individuals. The one-dimensional SDE algorithm was previously shown to be more efficient in detecting amplitude variations and in computational calculations (compared to other entropy algorithms). Additionally, we also extracted first-order statistical parameters like standard deviation (SD), and mean of positive pixels (MPP), among others. These MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> and first-order texture features were used to detect TB in each lung individually. The MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> was validated using a synthetic dataset and optimized for the best set of parameters. We verified that, for both lungs, the MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> values were significantly different between healthy and TB CXR images (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>), and the effect size was <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.23. From the first-order parameters, only the mean, SD, entropy, and MPP were statistically different between both groups for the left lung (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>; <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.22). For the right lung, all first-order features significantly differentiated TB patients (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>; <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.28). Finally, we show that a multi-layer perceptron obtained 86.4 and 85.2% accuracy in detecting TB in the left and right lungs, respectively. The highest sensitivity values achieved in this study were 71.4% and 81.8% for the left and right lungs, respectively.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108346"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614750","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}

{"title":"Effective muscle synergies extraction pipeline to characterise the Box and Block Test movement","authors":"Emma Colamarino ,&nbsp;Jlenia Toppi ,&nbsp;Floriana Pichiorri ,&nbsp;Valeria de Seta ,&nbsp;Giulia Savina ,&nbsp;Ilaria Mileti ,&nbsp;Eduardo Palermo ,&nbsp;Donatella Mattia ,&nbsp;Febo Cincotti","doi":"10.1016/j.bspc.2025.108252","DOIUrl":"10.1016/j.bspc.2025.108252","url":null,"abstract":"<div><div>The Box and Block test (BBT) is a clinical test for the evaluation of the unilateral manual dexterity. To the best of our knowledge, no study has yet explored it through the muscle synergy approach. Since each analysis step impacts on the muscle synergies influencing the results interpretation, this study aims at i) optimising the procedure of muscular synergies extraction in BBT and ii) applying it to characterise BBT in healthy individuals. Electromyographic (EMG) data (8 muscles per arm) were recorded from 16 participants during the BBT performed with each upper limb, separately. Muscle synergies were extracted by means of the Non-Negative Matrix Factorization method and the impact of following two parameters was estimated: (i) cut-off frequency of low-pass filter designed to compute the envelope of the EMG signals (ii) type of normalisation to be applied to the EMG envelopes. Results show that the procedure of muscular synergies extraction requires to be optimised with particular attention to the cut-off frequency of low-pass filter designed to compute the envelope of the EMG signals and the type of normalisation to be applied to the EMG envelopes. From the physiological perspective, three synergies seem to be enough to capture main BBT movement mechanisms. The evaluation of the impact of processing steps on the muscle synergies and the characterization of the BBT will pave the way for the definition of a standard procedure to be applied also in a pathological context.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108252"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623669","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}

{"title":"GSC-ABTA: A group-level brain sources connectivity framework based on adaptive block tensor analysis","authors":"Bashir Najafabadian ,&nbsp;Ali Motie Nasrabadi ,&nbsp;Saeid Rashidi","doi":"10.1016/j.bspc.2025.108336","DOIUrl":"10.1016/j.bspc.2025.108336","url":null,"abstract":"<div><h3>Background</h3><div>This study presents a group analysis method for identifying shared brain connectivity patterns using tensor analysis. The method’s efficacy is evaluated through a validation framework, considering various scenarios of group brain data generation and diverse control parameters.</div></div><div><h3>Methodology</h3><div>The proposed group estimation method for source-level brain connectivity begins by modeling the activity of brain and noise sources using a quasi-real six-layer head model to solve the direct problem. Pseudo-EEG data are then generated at the group level for three scenarios: Volume Conduction Effect (VC), Inter-Trial Variability (ITV), and Time Varying Connectivity (TV). The Group-Level Source Connectivity based on Adaptive Block Tensor Analysis (GSC-ABTA) is used to solve the inverse problem and estimate group-level source activity. This method allows for trial-dependent streaming updates in the group estimation of brain sources. Finally, a tensorial multivariate autoregressive model is developed in an adaptive format, taking into account a forgetting parameter for determining the contribution of observations in estimating effective brain connectivity coefficients at the group level. Statistical analysis was performed for six control parameters (including data length, signal-to-noise ratio, density, percentage of real connections added to the model, model order, and the number of trials) and compared with tensorial and non-tensorial methods in the three proposed scenarios. Additionally, the framework was validated with real data.</div></div><div><h3>Results</h3><div>The proposed method outperforms other methods in the VC scenario for all control parameters and in the ITV and TV scenarios for most control parameters. These findings underscore the importance of adaptive updating in extracting the activity of the sources for group investigation, facilitating the group extraction of brain connectivity coefficients on a more generalizable scale.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108336"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613821","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}

{"title":"Evaluating the impact of blood rheology in hemodynamic parameters by 4D Flow MRI in large vessels considering the hematocrit effect","authors":"Hernán Mella ,&nbsp;Felipe Galarce ,&nbsp;Tetsuro Sekine ,&nbsp;Julio Sotelo ,&nbsp;Ernesto Castillo","doi":"10.1016/j.bspc.2025.108145","DOIUrl":"10.1016/j.bspc.2025.108145","url":null,"abstract":"<div><div>Aortic hemodynamic parameters estimated from 4D Flow Magnetic Resonance (MR) velocity measurements are often estimated using a constant Newtonian viscosity, neglecting blood’s shear-thinning behavior. The aim of this work is to estimate and assess whether Newtonian viscosity is sufficient to quantify these parameters, given the non-Newtonian nature of blood. Additionally, we demonstrate that shear-thinning effects remain observable in large vessels despite artifacts commonly present in 4D Flow MR images.. To address this, we quantified the impact of blood rheology and hematocrit (Hct) on Wall Shear Stress (WSS), the rate of viscous Energy Loss (<span><math><msub><mrow><mover><mrow><mi>E</mi></mrow><mrow><mo>̇</mo></mrow></mover></mrow><mrow><mi>L</mi></mrow></msub></math></span>), and the Oscillatory Shear Index (OSI) based on velocity data obtained from 4D Flow MR images. Using a Hct-dependent power-law non-Newtonian model with experimentally derived rheological parameters, we analyzed these metrics across a broad range of Hct values at physiological temperatures in both in-silico and in-vivo MR datasets.</div><div>The results reveal significant differences between Newtonian and non-Newtonian models. In in-silico experiments, WSS and <span><math><msub><mrow><mover><mrow><mi>E</mi></mrow><mrow><mo>̇</mo></mrow></mover></mrow><mrow><mi>L</mi></mrow></msub></math></span> differed by up to +189% and +112% at systole, with reductions of −74% and −80% at diastole, respectively, while OSI differences ranged from −23% to −30%. For in-vivo data, WSS and <span><math><msub><mrow><mover><mrow><mi>E</mi></mrow><mrow><mo>̇</mo></mrow></mover></mrow><mrow><mi>L</mi></mrow></msub></math></span> deviations reached −44% and −60% at systole, ranging from −69% to +73% at diastole, with OSI differences averaging −21%. These findings highlights the importance of accounting for non-Newtonian blood rheology when estimating hemodynamic parameters from 4D Flow MR images in large vessels, enhancing the accuracy of cardiovascular disease assessments using in-vivo aortic data.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108145"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604624","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}

{"title":"A Knowledge-Driven Evidence Fusion Network for pancreatic tumor segmentation in CT images","authors":"Kaiqi Dong ,&nbsp;Yan Zhu ,&nbsp;Yu Tian ,&nbsp;Peijun Hu ,&nbsp;Chengkai Wu ,&nbsp;Xiang Li ,&nbsp;Tianshu Zhou ,&nbsp;Xueli Bai ,&nbsp;Tingbo Liang ,&nbsp;Jingsong Li","doi":"10.1016/j.bspc.2025.108281","DOIUrl":"10.1016/j.bspc.2025.108281","url":null,"abstract":"<div><div>Accurate pancreatic tumor segmentation remains challenging due to complex anatomical structures and diverse tumor appearances. This study presents a Knowledge-Driven Evidence Fusion Segmentation Network (KEFS-Net), a framework that systematically integrates radiological and anatomical knowledge from medical reports with imaging features to enhance segmentation accuracy. KEFS-Net consists of three key components: (1) a knowledge-driven attention network that leverages large language models, discrete information bottleneck, and cross-attention to enhance CT image segmentation performance by capturing informative features from medical reports, (2) an evidence fusion strategy based on Dempster–Shafer theory that optimizes segmentation results by evaluating the consistency between textual knowledge and image predictions, and (3) a masked learning approach that ensures robust performance in clinical scenarios with incomplete tumor descriptions. The framework was evaluated on both the Medical Segmentation Decathlon (MSD) dataset and an external clinical dataset from the First Affiliated Hospital (FAH) of Zhejiang University School of Medicine. Experimental results demonstrate superior performance compared to state-of-the-art methods, achieving Dice of 59.10% and 59.42% respectively for tumor segmentation on the MSD and external dataset. The approach shows particular strength in handling diverse tumor characteristics including size variations, boundary ambiguity, and complex anatomical locations. This knowledge-driven framework represents a significant advancement in leveraging domain knowledge through multi-modal integration for improved pancreatic tumor segmentation. Our code is available at <span><span>https://github.com/Singlesnail/KEFS-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108281"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614239","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}

{"title":"SPSNet: A spiking neural network with relation graphs for sleep stage classification based on polysomnography","authors":"Yuchen Pan,&nbsp;Kebin Jia,&nbsp;Zheng Jin,&nbsp;Zhe Li","doi":"10.1016/j.bspc.2025.108227","DOIUrl":"10.1016/j.bspc.2025.108227","url":null,"abstract":"<div><div>Sleep is crucial to human health, and in recent years, automatic sleep stage classification based on polysomnography(PSG) has become a hot topic in sleep science research. With the rapid development of artificial intelligence technology, especially the wide application of deep learning methods, the research on automatic sleep stage classification has made significant progress. However, existing methods mainly focus on time–frequency feature extraction and channel selection of signals, often ignoring the deep impact of biological mechanisms such as neuronal impulses on sleep stage classification. To this end, we propose a deep learning model called SPSNet, which innovatively introduces the impulse mechanism of a spiking neural network(SNN) and the structure of relational graph based on the transformation of the Watts–Strogatz(WS) small-world network into the epoch-level multi-channel sleep feature fusion process. This design not only achieves efficient sparse computation through SNN, but also enhances the interaction between neurons and improves the overall model performance through the relational graph structure. Experimental results on three public datasets(UCD, SleepEDF-78, HMC) show that SPSNet significantly improves the classification performance while effectively reducing network complexity and energy consumption compared to the baseline model approach, the accuracy(ACC) on the three datasets were 0.772, 0.807, and 0.775, the F1-score(MF1) were 0.761, 0.758, and 0.756, and the Cohen’s Kappa(<span><math><mi>κ</mi></math></span>) was 0.703, 0.739, and 0.706, representing improvements of 0.3% to 1.8% over the respective best baseline models. Overall, our work provides a new way of thinking for automatic sleep stage classification that combines spiking neural networks with relational graph structures.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108227"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604623","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}

{"title":"Stroke prediction algorithm based on 3D convolutional neural network for CT scans in patients with atrial fibrillation","authors":"Wei-Yu Hsu ,&nbsp;Cries Avian ,&nbsp;Jenq-Shiou Leu ,&nbsp;Chia-Ti Tsai","doi":"10.1016/j.bspc.2025.108338","DOIUrl":"10.1016/j.bspc.2025.108338","url":null,"abstract":"<div><div>Stroke, commonly associated with cerebral infarction, is a severe condition caused by disrupted blood flow to the brain. Atrial Fibrillation (AF), a prevalent cardiac arrhythmia, significantly increases the risk of stroke, with AF patients having a tenfold higher likelihood of stroke compared to the general population. Early detection of AF and the presence of blood clots is crucial for stroke prevention. In this study, we propose an AI-assisted diagnostic system based on a 3D convolutional neural network (CNN) trained on cardiac CT images to predict the risk of stroke in AF patients. Compared to traditional 2D CNN models, the proposed 3D CNN approach effectively captures 3D spatial features of cardiac structures, resulting in improved accuracy and performance. The 3D CNN model achieved an impressive accuracy of 92.92% and an AUC of 0.97 on the test set. The findings highlight the potential of AI-assisted diagnosis and the significance of utilizing cardiac CT images in enhancing cardiovascular disease diagnosis. This approach offers promising opportunities to improve accuracy, efficiency, and clinical decision-making in stroke prevention. Future research should focus on expanding the dataset, optimizing the model architecture, and integrating additional clinical data further to enhance the predictive performance of the AI model.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108338"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604705","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}

{"title":"Deep-Fusion: A lightweight feature fusion model with Cross-Stream Attention and Attention Prediction Head for brain tumor diagnosis","authors":"Abdul Haseeb Nizamani ,&nbsp;Zhigang Chen ,&nbsp;Uzair Aslam Bhatti","doi":"10.1016/j.bspc.2025.108305","DOIUrl":"10.1016/j.bspc.2025.108305","url":null,"abstract":"<div><div>The accurate and early detection of brain tumor types, such as gliomas, meningiomas, and pituitary tumors, is crucial for effective treatment planning and improving patient outcomes. However, advanced Computer-Aided Diagnosis (CAD) systems often face significant limitations in resource-constrained healthcare settings due to their high computational demands. State-of-the-art deep learning models often require substantial computational power and storage due to their complex architectures, large number of parameters, and model size which limits their practical applicability in such environments. To address this, we present Deep-Fusion, a novel lightweight model that maintains high accuracy while significantly reducing computational overhead, making it ideal for resource-constrained environments. Our proposed model leverages the strengths of two lightweight pre-trained models, MobileNetV2 and EfficientNetB0, integrated through the Feature Fusion Module (FFM), which is further enhanced by the Lightweight Feature Extraction Module (LEM), Cross-Stream Attention (CSA), and an Attention Prediction Head (APH). These components work together to optimize feature representation while preserving computational efficiency. We evaluated Deep-Fusion on two brain MRI datasets, Figshare and Br35H, achieving outstanding accuracies of 99.19% and 99.83%, respectively. Additionally, the model demonstrated exceptional performance in precision, recall, and F1-score metrics, recording 99.19%, 99.11%, and 99.15% on the Figshare dataset, and 99.83% across all metrics on the Br35H dataset. These findings establish Deep-Fusion as a reliable and efficient tool for medical image analysis, particularly in environments with limited computational resources.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108305"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604622","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}

{"title":"TSAA-Net: Triple-semantic-aware attention network for colorectal cancer grading from histology images","authors":"Xu Wang ,&nbsp;Deyi Wang ,&nbsp;Dan Deng ,&nbsp;Yamei Deng","doi":"10.1016/j.bspc.2025.108310","DOIUrl":"10.1016/j.bspc.2025.108310","url":null,"abstract":"<div><div>Colorectal cancer grading is crucial for follow-up treatment and overall patient prognosis. However, it is challenging to perform clinical practice because of these problems: 1) the class token derived from small image patches inadequately represents the label information of the entire histological image; 2) image patches often causes distortion in token feature details across the full image; 3) small patches always fail to incorporate the entire tissue micro-architecture, missing regions of interest. To solve such problems, a Triple-Semantic-Aware Attention Network (TSAA-Net) is proposed for colorectal cancer grading from histology images, where the Class-Token Semantic Aware Attention (CLTSAA) module is devised to capture global information by learning class tokens from different classifications; then, the Channel-Token Semantic Aware Attention (CHTSAA) module is designed to compensate for feature detail loss by refining local pixel-level information; finally, a Space-Token Semantic Aware Attention (SPTSAA) module is developed to integrate the entire tissue micro-architecture by capturing dual attention space-token information. Experiments results on two colorectal cancer datasets demonstrate the effectiveness of the proposed TSAA-Net, providing valuable support for pathologists in the grading of colorectal cancer.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108310"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595772","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}