Computer methods and programs in biomedicine最新文献

{"title":"Enhancing thrombosis prevention in medical devices: The role of turbulence in washout performance using FDA benchmark nozzle model","authors":"Peng Fang ,&nbsp;Peng Wu ,&nbsp;Haiquan Feng ,&nbsp;Haimei Huang","doi":"10.1016/j.cmpb.2025.108647","DOIUrl":"10.1016/j.cmpb.2025.108647","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Thrombosis presents a significant and potentially lethal risk in medical devices. Turbulence has been associated with increased thrombosis risk, primarily due to heightened shear stress and resultant blood damage. However, it can be inferred that turbulence might also enhance washout performance through efficient transport and mixing, thereby mitigating thrombosis. This study explores the underappreciated role of turbulence.</div></div><div><h3>Methods</h3><div>The FDA benchmark nozzle model was used as a representative framework for medical devices. To elucidate the isolated role of turbulence on washout performance, comparative simulations were conducted at Reynolds numbers of 500 and 6500 using Large Eddy Simulation (LES) and Menter's Shear Stress Transport (SST) k-ω turbulence models. Washout performance, a critical indicator in thrombosis, is evaluated by a passive scalar transport model.</div></div><div><h3>Results</h3><div>The validation results align well with published data, confirming the reliability of the simulations. Reynolds numbers and turbulence models play a crucial role in the washout performance. Turbulence improves volume washout by disrupting flow recirculation zones and enhancing the mixing of old and new blood. Furthermore, turbulence aids in surface washout by altering flow patterns in the near-wall region and increasing wall shear stress.</div></div><div><h3>Conclusion and significance</h3><div>The improved washout and dynamic environment facilitated by turbulence potentially minimize platelet adhesion and aggregation, which ultimately benefits the anti-thrombotic properties of medical devices. This research offers a novel perspective on the role of turbulence in thrombosis, extending beyond its traditionally recognized detrimental effects, and provides valuable insights into the design of specific flow patterns in achieving optimal washout performance in medical device applications. Further research is warranted to explore how to effectively leverage the washout-enhancing effects of turbulence while minimizing its potential adverse impacts.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108647"},"PeriodicalIF":4.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372398","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":"Standard plane localization using denoising diffusion model with multi-scale guidance","authors":"Haoran Dou ,&nbsp;Yuhao Huang ,&nbsp;Yunzhi Huang ,&nbsp;Xin Yang ,&nbsp;Chaojiong Zhen ,&nbsp;Yuanji Zhang ,&nbsp;Yi Xiong ,&nbsp;Weijun Huang ,&nbsp;Dong Ni","doi":"10.1016/j.cmpb.2025.108619","DOIUrl":"10.1016/j.cmpb.2025.108619","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Standard planes (SPs) acquisition is a fundamental yet crucial step in routine ultrasound (US) examinations. Compared to the 2D US, 3D US offers the advantage of capturing multiple SPs in a single scan, and visualizing particular SPs (e.g., the coronal plane of the uterus). However, SPs localization in 3D US is challenging due to the vast 3D search space, anatomical variability, and poor image quality.</div></div><div><h3>Methods:</h3><div>In this study, we present a probabilistic method based on the conditional denoising diffusion model for SPs localization in 3D US. Specifically, we construct multi-scale guidance to provide the model with both global and local context. We improve the model’s angular sensitivity by modifying the tangent-based plane representation with the spherical coordinates. We also reveal the potential in simultaneously localizing SPs and detecting their abnormality without introducing extra parameters.</div></div><div><h3>Results:</h3><div>Extensive validations were performed on a large in-house dataset containing 837 patients across two organs with four SPs. The proposed method achieved average errors of less than <span><math><mrow><mn>10</mn><mo>°</mo></mrow></math></span> and 1 mm in terms of the angle and distance on the four investigated SPs. Furthermore, it can obtain over 90% accuracy for detecting anomalies by simply thresholding the quantified uncertainty.</div></div><div><h3>Conclusions:</h3><div>The results show that our proposed method significantly outperformed the current state-of-the-art approaches regarding spatial and content metrics across four SPs in two organs, indicating its superiority and generalizability. Meanwhile, the investigated anomaly detection of our method demonstrates its potential in applying clinical practice.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108619"},"PeriodicalIF":4.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294121","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":"Antimicrobial resistance recommendations via electronic health records with graph representation and patient population modeling","authors":"Pei Gao ,&nbsp;Zheng Chen ,&nbsp;Xin Liu ,&nbsp;Peng Chen ,&nbsp;Yasuko Matsubara ,&nbsp;Yasushi Sakurai","doi":"10.1016/j.cmpb.2025.108616","DOIUrl":"10.1016/j.cmpb.2025.108616","url":null,"abstract":"<div><h3>Background:</h3><div>Antimicrobial resistance (AMR), which refers to the ability of pathogenic bacteria to withstand the effects of antibiotics, is a critical global health issue. Traditional methods for identifying AMRs in clinical settings rely on in-lab testing, which hampers timely medical decision-making. Moreover, there is a notable delay in updating empirical treatment guidelines in response to the rapid evolution of pathogens. Recent advances in AMR research have illuminated the potential of machine learning-based patient information analysis using electronic health records (EHRs).</div></div><div><h3>Methods:</h3><div>Against this backdrop, our study introduces a novel deep learning framework designed to leverage EHR data for generating AMR recommendations. This framework is anchored in three critical innovations. Firstly, we employ a deep graph neural network to model the correlations between various medical events, using structural information to enhance the representation of binary medical events. Secondly, in acknowledgment of the commonalities in pathogen evolution among populations, we incorporate population-level observation by modeling patient graphical structures. This strategy also addresses the issue of imbalance in rare AMR labels. Finally, we adopt a multi-task learning strategy, enabling simultaneous recommendations on multiple AMRs. Extensive experimental evaluations on a large dataset of over 110,000 patients with urinary tract infections validate the superiority of our approach.</div></div><div><h3>Results:</h3><div>It achieves notable improvements in areas under receiver operating characteristic curves (AUROCs) for four distinct AMR labels, with increments of 0.04, 0.02, 0.06, and 0.10 surpassing the baselines.</div></div><div><h3>Conclusions:</h3><div>Further medical analysis underscores the efficacy of our approach, demonstrating the potential of EHR-based systems in AMR recommendation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108616"},"PeriodicalIF":4.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries","authors":"Frederic Jonske ,&nbsp;Moon Kim ,&nbsp;Enrico Nasca ,&nbsp;Janis Evers ,&nbsp;Johannes Haubold ,&nbsp;René Hosch ,&nbsp;Felix Nensa ,&nbsp;Michael Kamp ,&nbsp;Constantin Seibold ,&nbsp;Jan Egger ,&nbsp;Jens Kleesiek","doi":"10.1016/j.cmpb.2025.108634","DOIUrl":"10.1016/j.cmpb.2025.108634","url":null,"abstract":"<div><h3>Purpose</h3><div>In medical deep learning, models not trained from scratch are typically fine-tuned based on ImageNet-pretrained models. We posit that pretraining on data from the domain of the downstream task should almost always be preferable.</div></div><div><h3>Materials and methods</h3><div>We leverage RadNet-12M and RadNet-1.28M, datasets containing &gt;12 million/1.28 million acquired CT image slices from 90,663 individual scans, and explore the efficacy of self-supervised, contrastive pretraining on the medical and natural image domains. We compare the respective performance gains for five downstream tasks. For each experiment, we report accuracy, AUC, or DICE score and uncertainty estimations based on four separate runs. We quantify significance using Welch's <em>t</em>-test. Finally, we perform feature space analysis to characterize the nature of the observed performance gains.</div></div><div><h3>Results</h3><div>We observe that intra-domain transfer (RadNet pretraining and CT-based tasks) compares favorably to cross-domain transfer (ImageNet pretraining and CT-based tasks), generally achieving comparable or improved performance – Δ = +0.44% (<em>p</em> = 0.541) when fine-tuned on RadNet-1.28M, Δ = +2.07% (<em>p</em> = 0.025) when linearly evaluating on</div><div>RadNet-1.28M, and Δ = +1.63% (<em>p</em> = 0.114) when fine-tuning on 1 % of RadNet-1.28M data. This intra-domain advantage extends to LiTS 2017, another CT-based dataset, but not to other medical imaging modalities. A corresponding intra-domain advantage was also observed for natural images. Outside the CT image domain, ImageNet-pretrained models generalized better than RadNet-pretrained models.</div><div>We further demonstrate that pretraining on medical images yields domain-specific features that are preserved during fine-tuning, and which correspond to macroscopic image properties and structures.</div></div><div><h3>Conclusion</h3><div>We conclude that intra-domain pretraining generally outperforms cross-domain pretraining, but that very narrow domain definitions apply. Put simply, pretraining on CT images instead of natural images yields an advantage when fine-tuning on CT images, and only on CT images. We further conclude that ImageNet pretraining remains a strong baseline, as well as the best choice for pretraining if only insufficient data from the target domain is available. Finally, we publish our pretrained models and pretraining guidelines as a baseline for future research.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108634"},"PeriodicalIF":4.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemodynamics of different surgical subclavian revascularization morphologies for thoracic endovascular aortic repair","authors":"Yining Zhang ,&nbsp;Zhongze Cao ,&nbsp;Xiran Cao ,&nbsp;Yue Che ,&nbsp;Xuelan Zhang ,&nbsp;Mingyao Luo ,&nbsp;Chang Shu","doi":"10.1016/j.cmpb.2025.108632","DOIUrl":"10.1016/j.cmpb.2025.108632","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Carotid-subclavian bypass (CSB) and subclavian-carotid transposition (SCT) are mainstream surgical left subclavian artery (LSA) revascularization methods. However, surgical selection of CSB and SCT morphological configurations mainly depends on surgeons’ experience, lacking objective data basis.</div></div><div><h3>Methods</h3><div>Geometries with 28 configurations, including length, diameter, angle, and anastomotic direction for prosthetic conduit and transposed LSA, were constructed. Numerical simulations were performed to evaluate CSB and SCT outcomes by hemodynamic parameters such as pressure drop, flow rate, energy loss and wall shear stress related indicators.</div></div><div><h3>Results</h3><div>After CSB, enlarging prosthetic conduit diameter (6 to 10 mm) increases flow rate by 36.64 %, suggesting larger diameter enhances LSA patency. However, when diameter exceeds 9 mm, the relative residence time rises by 35.29 %, demonstrating oversized diameter increases the risk of thrombosis. Compared to 5 mm, prosthetic conduit at 15 mm displays a 7.80 % flow rate reduction, indicating longer conduit causes greater flow resistance. For varying angles, prosthetic conduit perpendicular to left common carotid artery (LCCA) shows the least energy loss. Conduit tilted downward from the vertical position shows higher flow rate than the upward during systole (210.35 vs. 106.34 ml/min). However, 10 % blood flow in downward conduit reflows cyclically during diastole, resulting in the reduced cycle-averaged flow rate of downward conduit compared to that of the upward (53.21 vs. 58.42 ml/min). After SCT, configurations with smaller angles between LCCA and LSA show better hemodynamic performance, with a maximum flow rate variation of 30.34 % in LSA from 50° to 110°.</div></div><div><h3>Conclusions</h3><div>Configurations with moderately smaller diameter, reduced length of prosthetic conduit and aligned anastomosis towards LCCA blood flow result in better LSA revascularization outcomes. The findings are supportive for optimizing CSB and SCT configurations.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108632"},"PeriodicalIF":4.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104379","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":"Unveiling Alzheimer’s disease through brain age estimation using multi-kernel regression network and magnetic resonance imaging","authors":"Raveendra Pilli,&nbsp;Tripti Goel,&nbsp;R. Murugan","doi":"10.1016/j.cmpb.2025.108617","DOIUrl":"10.1016/j.cmpb.2025.108617","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Structural magnetic resonance imaging (MRI) studies have unveiled age-related anatomical changes across various brain regions. The disparity between actual age and estimated age, known as the Brain-Predicted Age Difference (Brain-PAD), serves as an indicator for predicting neurocognitive ailments or brain abnormalities resulting from diseases. This study aims to develop an accurate brain age prediction model that can assist in identifying potential neurocognitive impairments.</div></div><div><h3>Methods:</h3><div>The present study implemented a brain age prediction model using a ResNet-50 deep network and a multi-kernel extreme learning machine (MKELM) regression network, relying on MRI images. Kernel methods translate input information into higher-dimensional space by introducing nonlinearity and enabling the model to grasp complicated data patterns. A multi-kernel function combines the Gaussian and polynomial kernels and is incorporated into the brain age regression model. The model effectively utilizes the benefits of both kernel functions to estimate the ages accurately. MRI scans are segmented into gray matter (GM) and white matter (WM) maps preprocessed and extracted of significant features using the ResNet-50 deep network. Extracted features of the WM and GM datasets are fed into the MKELM regression model for brain age prediction.</div></div><div><h3>Results:</h3><div>The proposed age estimation framework achieved 3.06 years of mean absolute error (MAE) and 4.12 years of root mean square error (RMSE) on healthy controls (HC) WM scans, and on GM scans, 2.73 years of MAE and 3.65 years of RMSE values. To further validate the importance of Brain-PAD as a biomarker for identifying brain health conditions, an independent testing dataset of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects age is predicted. The Brain-PAD values for AD subjects’ GM images are significantly higher compared to those of HC and MCI subjects, indicating distinct brain health conditions. Furthermore, variations in GM and WM tissue were identified in AD subjects, revealing that the parahippocampus and corpus callosum were notably affected.</div></div><div><h3>Conclusion:</h3><div>Our findings underscore the potential of Brain-PAD as a significant biomarker for assessing brain health, with implications for early detection of neurocognitive diseases. The developed framework effectively estimates brain age using MRI, contributing valuable insights into the relationship between brain structure and cognitive health.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108617"},"PeriodicalIF":4.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095288","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":"Enhancing multimodal medical image analysis with Slice-Fusion: A novel fusion approach to address modality imbalance","authors":"Awais Ahmed ,&nbsp;Xiaoyang Zeng ,&nbsp;Rui Xi ,&nbsp;Mengshu Hou ,&nbsp;Syed Attique Shah","doi":"10.1016/j.cmpb.2025.108615","DOIUrl":"10.1016/j.cmpb.2025.108615","url":null,"abstract":"<div><h3>Background and objective:</h3><div>In recent times, medical imaging analysis (MIA) has seen an increasing interest due to its core application in computer-aided diagnosis systems (CADs). A modality in MIA refers to a specific technology used to produce human body images, such as MRI, CT scans, or X-rays. Each modality presents unique challenges and characteristics, often leading to imbalances within datasets. This significant challenge impedes model training and generalization due to the varying convergence rates of different modalities and the suppression of gradients in less dominant modalities.</div></div><div><h3>Methods:</h3><div>This paper proposes a novel fusion approach, and we named it Slice-Fusion. The proposed approach aims to mitigate the modality imbalance problem by implementing a “Modality-Specific-Balancing-Factor” fusion strategy. Furthermore, it incorporates an auxiliary (uni-modal) task that generates balanced modality pairs based on the image orientations of different modalities. Subsequently, a novel multimodal classification framework is presented to learn from the generated balanced modalities. The effectiveness of the proposed approach is evaluated through comparative assessments on a publicly available BraTS2021 <span><span>dataset</span><svg><path></path></svg></span>. The results demonstrate the efficiency of Slice-Fusion in resolving the modality imbalance problem. By enhancing the representation of balanced features and reducing modality bias, this approach holds promise for advancing visual health informatics and facilitating more accurate and reliable medical image analysis.</div></div><div><h3>Results:</h3><div>In the experiment section, three diverse experiments are conducted such as i) Fusion Loss Metrics Evaluation, ii) Classification, and iii) Visual Health Informatics. Notably, the proposed approach achieved an F1-Score of (100%, 81.25%) on the training and validation sets for the classification generalization task. In addition to the Slice-Fusion’s out-performance, the study also created a new modality-aligned dataset (a highly balanced and informative modality-specific image collection) that aids further research and improves MIA’s robustness. These advancements not only enhance the capability of medical diagnostic tools but also create opportunities for future innovations in the field.</div></div><div><h3>Conclusion:</h3><div>This study contributes to advancing medical image analysis, such as effective modality fusion, image reconstruction, comparison, and glioma classification, facilitating more accurate and reliable results, and holds promise for further advancements in visual health informatics.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108615"},"PeriodicalIF":4.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094869","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":"M2OCNN: Many-to-One Collaboration Neural Networks for simultaneously multi-modal medical image synthesis and fusion","authors":"Jian Zhang,&nbsp;Xianhua Zeng","doi":"10.1016/j.cmpb.2025.108612","DOIUrl":"10.1016/j.cmpb.2025.108612","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Acquiring comprehensive information from multi-modal medical images remains a challenge in clinical diagnostics and treatment, due to complex inter-modal dependencies and missing modalities. While cross-modal medical image synthesis (CMIS) and multi-modal medical image fusion (MMIF) address certain issues, existing methods typically treat these as separate tasks, lacking a unified framework that can generate both synthesized and fused images in the presence of missing modalities.</div></div><div><h3>Methods:</h3><div>In this paper, we propose the Many-to-One Collaboration Neural Network (M2OCNN), a unified model designed to simultaneously address CMIS and MMIF. Unlike traditional approaches, M2OCNN treats fusion as a specific form of synthesis and provides a comprehensive solution even when modalities are missing. The network consists of three modules: the Parallel Untangling Hybrid Network, Comprehensive Feature Router, and Series Omni-modal Hybrid Network. Additionally, we introduce a mixed-resolution attention mechanism and two transformer variants, Coarsormer and ReCoarsormer, to suppress high-frequency interference and enhance model performance.</div><div>M2OCNN outperformed state-of-the-art methods on three multi-modal medical imaging datasets, achieving an average PSNR improvement of 2.4 dB in synthesis tasks and producing high-quality fusion images despite missing modalities. The source code is available at <span><span>https://github.com/zjno108/M2OCNN</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusion:</h3><div>M2OCNN offers a novel solution by unifying CMIS and MMIF tasks in a single framework, enabling the generation of both synthesized and fused images from a single modality. This approach sets a new direction for research in multi-modal medical imaging, with implications for improving clinical diagnosis and treatment.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108612"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094871","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":"Semi-supervised Strong-Teacher Consistency Learning for few-shot cardiac MRI image segmentation","authors":"Yuting Qiu ,&nbsp;James Meng ,&nbsp;Baihua Li","doi":"10.1016/j.cmpb.2025.108613","DOIUrl":"10.1016/j.cmpb.2025.108613","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular disease is a leading cause of mortality worldwide. Automated analysis of heart structures in MRI is crucial for effective diagnostics. While supervised learning has advanced the field of medical image segmentation, it however requires extensive labelled data, which is often limited for cardiac MRI.</div></div><div><h3>Methods:</h3><div>Drawing on the principle of consistency learning, we introduce a novel semi-supervised Strong-Teacher Consistency Network for few-shot multi-class cardiac MRI image segmentation, leveraging largely available unlabelled data. This model incorporates a student–teacher architecture. A multi-teacher structure is introduced to learn diverse perspectives and avoid local optimals when dealing with largely varying cardiac structures and anatomical features. It employs a hybrid loss that emphasizes consistency between student and teacher representations, alongside supervised losses (e.g., Dice and Cross-entropy), tailored to the challenge of unlabelled data. Additionally, we introduced feature-space virtual adversarial training to enhance robust feature learning and model stability.</div></div><div><h3>Results:</h3><div>Evaluation and ablation studies on the MM-WHS and ACDC benchmark datasets show that the proposed model outperforms nine state-of-the-art semi-supervised methods, particularly with limited annotated data. It achieves 90.14% accuracy on MM-WHS and 78.45% accuracy on ACDC at labelling rates of 25% and 1%, respectively. It also highlights its unique advantages over fully-supervised and single-teacher approaches.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108613"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification","authors":"Yingfan Ma ,&nbsp;Mingzhi Yuan ,&nbsp;Ao Shen ,&nbsp;Xiaoyuan Luo ,&nbsp;Bohan An ,&nbsp;Xinrong Chen ,&nbsp;Manning Wang","doi":"10.1016/j.cmpb.2025.108614","DOIUrl":"10.1016/j.cmpb.2025.108614","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost of detailed patch-level annotations. Existing MIL methods primarily focus on bag-level classification, often overlooking critical instance-level information, which results in suboptimal outcomes. This paper proposes a novel semi-supervised learning approach, SeLa-MIL, which leverages both labeled and unlabeled instances to improve instance and bag classification, particularly in hard positive instances near the decision boundary.</div></div><div><h3>Methods</h3><div>SeLa-MIL reformulates the traditional MIL problem as a novel semi-supervised instance classification task to effectively utilize both labeled and unlabeled instances. To address the challenge where all labeled instances are negative, we introduce a weakly supervised self-training framework by solving a constrained optimization problem. This method employs global and local constraints on pseudo-labels derived from positive WSI information, enhancing the learning of hard positive instances and ensuring the quality of pseudo-labels. The approach can be integrated into end-to-end training pipelines to maximize the use of available instance-level information.</div></div><div><h3>Results</h3><div>Comprehensive experiments on synthetic datasets, MIL benchmarks, and popular WSI datasets demonstrate that SeLa-MIL consistently outperforms existing methods in both instance and bag-level classification, with substantial improvements in recognizing hard positive instances. Visualization further highlights the method’s effectiveness in pathology regions relevant to cancer diagnosis.</div></div><div><h3>Conclusion</h3><div>SeLa-MIL effectively addresses key challenges in MIL-based WSI classification by reformulating it as a semi-supervised problem, leveraging both weakly supervised learning and pseudo-labeling techniques. This approach improves classification accuracy and generalization across diverse datasets, making it valuable for pathology image analysis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108614"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294124","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}