Computer methods and programs in biomedicine update最新文献

Towards objective In-Vitro wound healing assessment with segment anything: A large evaluation of interactive and automated pipelines 面向客观的体外切口愈合评估：交互式和自动化管道的大规模评估

Computer methods and programs in biomedicine update

Pub Date : 2025-12-16 DOI: 10.1016/j.cmpbup.2025.100224

Katja Löwenstein , Johanna Rehrl , Anja Schuster , Michael Gadermayr

The in vitro scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is subjective and vulnerable to intra- and interobserver variability, computer-based tools are theoretically objective, but in practice often contain parameters which are manually adjusted (individually per image or data set) and thereby provide a source for subjectivity. Modern deep learning approaches typically require large annotated training data which complicates instant applicability. In this paper, we deeply investigate the Segment Anything Model (SAM), a deep foundation model based on interactive point-prompts, which enables class-agnostic segmentation without tuning the network’s parameters based on any domain specific training data. With respect to segmentation accuracy, the interactive method significantly outperformed a semi-objective baseline that required manual inspection and, when necessary, parameter adjustments for each image. Experiments were conducted to evaluate the impact of variability due to interactive prompting. The results exhibited remarkably low intra- and interobserver variability, clearly surpassing the consistency of manual segmentation by domain experts. In addition, a fully automated zero-shot approach was explored, incorporating the self-supervised learning model DINOv2 as a preprocessing step before sampling input points for SAM, with various sampling methods systematically investigated.

体外划痕实验是细胞生物学中广泛使用的一种实验，用于评估与各种治疗干预相关的伤口愈合率。虽然人工测量是主观的，容易受到观察者内部和观察者之间变化的影响，但基于计算机的工具在理论上是客观的，但在实践中往往包含手动调整的参数（每个图像或数据集单独），从而提供了主观性的来源。现代深度学习方法通常需要大量带注释的训练数据，这使得即时适用性变得复杂。本文深入研究了基于交互式点提示的深度基础模型SAM (Segment Anything Model)，该模型无需根据任何特定领域的训练数据调整网络参数，即可实现与类别无关的分割。在分割精度方面，交互式方法显著优于半客观基线，后者需要人工检查，并在必要时对每个图像进行参数调整。我们进行了实验来评估交互提示引起的变异性的影响。结果显示观察者内部和观察者之间的可变性非常低，明显超过了领域专家手工分割的一致性。此外，探索了一种全自动零采样方法，将自监督学习模型DINOv2作为SAM输入点采样前的预处理步骤，并对各种采样方法进行了系统研究。

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引用次数: 0

Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability 口咽鳞癌 HPV 状态的临床特征预测分析：一种具有可解释性的机器学习方法

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2024.100170

Emily Diaz Badilla , Ignasi Cos , Claudio Sampieri , Berta Alegre , Isabel Vilaseca , Simone Balocco , Petia Radeva

Background and Objective:

Oropharynx Squamous Cell Carcinoma (OPSCC) linked to Human Papillomavirus (HPV) exhibits a more favorable prognosis than other squamous cell carcinomas of the upper aerodigestive tract. Finding reliable non-invasive detection methods of this prognostic entity is key to propose appropriate therapeutic decisions. This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features.

Materials and Methods:

We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. For explainability, SHAP and LIME were used to identify the most relevant relationships and their effect on the predictive model. Furthermore, additional publicly available datasets were scrutinized to compare outcomes and assess the method’s generalization across diverse feature sets and populations.

Results:

The best model yielded an AUC of 0.85, a sensitivity of 0.83, and a specificity of 0.75 over the testing set. The explainability analysis highlighted the remarkable significance of specific clinical attributes, in particular the oropharynx subsite tumor location and the patient’s smoking history. The contribution of each variable to the prediction was substantiated by creating a 95% confidence intervals of model coefficients by means of a 10,000 sample bootstrap and by analyzing top contributors across the best-performing models.

Conclusions:

The combination of specific clinical factors typically collected for OPSCC patients, such as smoking habits and the tumor oropharynx sub-location, along with the ML models hereby presented, can by themselves provide an informed analysis of the HPV status, and of proper use of data science techniques to explain it. Future work should focus on adding other data modalities such as CT scans to enhance performance and to uncover new relations, thus aiding medical practitioners in diagnosing OPSCC more accurately.

背景与目的：与人乳头状瘤病毒（HPV）相关的口咽鳞状细胞癌（OPSCC）表现出比其他上呼吸道鳞状细胞癌更好的预后。寻找可靠的非侵入性检测方法是提出适当治疗决策的关键。本研究旨在提供一种基于治疗前临床数据的综合方法来预测患者的HPV状态，并采用可解释性技术来解释这些特征的意义和影响。材料和方法：我们使用RADCURE数据集临床信息来训练六种机器学习算法，通过网格搜索超参数调整和特征选择的交叉验证来评估它们，并在20%的样本测试集上进行最终性能测量。为了可解释性，我们使用SHAP和LIME来确定最相关的关系及其对预测模型的影响。此外，还仔细检查了其他公开可用的数据集，以比较结果并评估该方法在不同特征集和人群中的泛化性。结果：最佳模型在测试集上的AUC为0.85，灵敏度为0.83，特异性为0.75。可解释性分析强调了特定临床属性的显著意义，特别是口咽部亚位肿瘤的位置和患者的吸烟史。每个变量对预测的贡献是通过创建模型系数的95%置信区间来证实的，方法是通过10,000个样本的自举，并通过分析表现最好的模型中的顶级贡献者。结论：结合OPSCC患者通常收集的特定临床因素，如吸烟习惯和肿瘤口咽亚位，以及本文提出的ML模型，可以单独提供对HPV状态的知情分析，并正确使用数据科学技术来解释它。未来的工作应侧重于增加其他数据模式，如CT扫描，以提高性能和发现新的关系，从而帮助医生更准确地诊断OPSCC。

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引用次数: 0

Multiscale guided attention network for optic disc segmentation of retinal images 视网膜图像视盘分割的多尺度引导注意网络

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100180

A Z M Ehtesham Chowdhury , Andrew Mehnert , Graham Mann , William H. Morgan , Ferdous Sohel

Optic disc (OD) segmentation from retinal images is crucial for diagnosing, assessing, and tracking the progression of several sight-threatening diseases. This paper presents a deep machine-learning method for semantically segmenting OD from retinal images. The method is named multiscale guided attention network (MSGANet-OD), comprising encoders for extracting multiscale features and decoders for constructing segmentation maps from the extracted features. The decoder also includes a guided attention module that incorporates features related to structural, contextual, and illumination information to segment OD. A custom loss function is proposed to retain the optic disc's geometrical shape (i.e., elliptical) constraint and to alleviate the blood vessels' influence in the overlapping region between the OD and vessels. MSGANet-OD was trained and tested on an in-house clinical color retinal image dataset captured during ophthalmodynamometry as well as on several publicly available color fundus image datasets, e.g., DRISHTI-GS, RIM-ONE-r3, and REFUGE1. Experimental results show that MSGANet-OD achieved superior OD segmentation performance from ophthalmodynamometry images compared to widely used segmentation methods. Our method also achieved competitive results compared to state-of-the-art OD segmentation methods on public datasets. The proposed method can be used in automated systems to quantitatively assess optic nerve head abnormalities (e.g., glaucoma, optic disc neuropathy) and vascular changes in the OD region.

从视网膜图像中分割视盘（OD）对于诊断、评估和跟踪几种威胁视力的疾病的进展至关重要。本文介绍了一种从视网膜图像中语义分割视盘的深度机器学习方法。该方法被命名为多尺度引导注意力网络（MSGANet-OD），包括用于提取多尺度特征的编码器和用于从提取的特征中构建分割图的解码器。解码器还包括一个引导注意力模块，该模块结合与结构、上下文和光照信息相关的特征来分割 OD。为了保留视盘的几何形状（即椭圆形）约束，并减轻血管在视盘和血管重叠区域的影响，提出了一种自定义损失函数。MSGANet-OD 在眼动力测定时捕获的内部临床彩色视网膜图像数据集以及几个公开的彩色眼底图像数据集（如 DRISHTI-GS、RIM-ONE-r3 和 REFUGE1）上进行了训练和测试。实验结果表明，与广泛使用的眼动力计图像分割方法相比，MSGANet-OD 的外径分割性能更为出色。与公共数据集上最先进的外径分割方法相比，我们的方法也取得了具有竞争力的结果。所提出的方法可用于自动系统，定量评估视神经头异常（如青光眼、视盘神经病变）和外径区域的血管变化。

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引用次数: 0

Independence on the lead of the identification of the ventricular depolarization in the electrocardiogram in wearable devices 独立于可穿戴设备中心电图心室去极化的识别

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100196

Noemi Giordano, Silvia Cannone, Gabriella Balestra, Marco Knaflitz

Goal

The home monitoring of cardiac time intervals reduces hospitalization and mortality of cardiovascular patients. However, a reliable time reference in the electrocardiogram is necessary. Nevertheless, the use of different single leads, typical of wearable devices, impacts the repeatability of the time reference and thus the accuracy of the time-dependent parameters. This work proposes a simple approach to detect the peak and onset of the ventricular depolarization, and demonstrates its lead independence, which makes it suitable for wearable devices even with non-standard leads.

Methods

Our method grounds on an energy-based approach, which we applied on a) a publicly available dataset with standard 12-lead recordings; b) a proof-of-concept dataset including a custom precordial non-standard lead implemented on a wearable device.

Results

Compared against the Pan-Tompkins algorithm, our method reduced the absolute error between each lead and the first standard lead by 26 % to 64 % for the peak, and by 70 % to 82 % for the onset detection. The achieved consistency across leads is compatible with clinical monitoring. The computational time was also reduced by 65 % to 96 %, making the algorithm suitable for use on microcontroller-based wearable devices.

Conclusions

The proposed method enables the identification of a stable reference of the ventricular depolarization regardless of the choice of the lead. The presented results open to the implementation on wearable devices for chronic disease monitoring purposes.

目的心脏时间间隔的家庭监测可降低心血管患者的住院率和死亡率。然而，在心电图中有一个可靠的时间参考是必要的。然而，使用不同的单引线（典型的可穿戴设备）会影响时间参考的可重复性，从而影响时间相关参数的准确性。这项工作提出了一种简单的方法来检测心室去极化的峰值和开始，并证明了它的引线独立性，这使得它适用于可穿戴设备，即使是非标准引线。该方法基于基于能量的方法，我们将其应用于a)具有标准12导联记录的公开可用数据集；B)概念验证数据集，包括在可穿戴设备上实现的自定义pre - cordial非标准lead。结果与Pan-Tompkins算法相比，该方法将每条导联与第一标准导联的峰值绝对误差降低了26% ~ 64%，在起始检测方面降低了70% ~ 82%。导联间的一致性与临床监测一致。计算时间也减少了65%至96%，使该算法适用于基于微控制器的可穿戴设备。结论无论选择何种导联，该方法都能确定稳定的心室去极化参考。提出的结果对用于慢性疾病监测目的的可穿戴设备的实施开放。

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引用次数: 0

On finite-time stability of some COVID-19 models using fractional discrete calculus 基于分数阶离散微积分的COVID-19模型有限时间稳定性研究

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100188

Shaher Momani , Iqbal M. Batiha , Issam Bendib , Abeer Al-Nana , Adel Ouannas , Mohamed Dalah

This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.

本研究研究了COVID-19分数阶（FO）离散易感-感染-恢复（SIR）模型的有限时间稳定性，并结合记忆效应来捕捉现实世界的流行动态。我们使用离散分数微积分分析无病平衡点和大流行平衡点的稳定性。理论框架介绍了基本定义，有限时间稳定性（FTS）标准，以及新的分数阶建模见解。数值模拟验证了各种参数下的理论结果，证明了该方法在有限时间内收敛于平衡状态。结果突出了FO模型在处理延迟响应和延长效应方面的灵活性，提供了比传统整数阶方法更高的预测精度。这项研究有助于设计有效的公共卫生干预措施和数学流行病学的进步。

引用次数: 0

Resectograms: Planning liver surgery with real-time occlusion-free visualization of virtual resections 切除图：通过虚拟切除的实时无闭塞可视化规划肝脏手术

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100186

Ruoyan Meng , Davit Aghayan , Egidijus Pelanis , Bjørn Edwin , Faouzi Alaya Cheikh , Rafael Palomar

Background and Objective:

Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. However, the intricate liver anatomical information often results in occlusions and visualization information clutter, which can lead to inaccuracies in virtual resections. To overcome these challenges, we introduce Resectograms, which are planar (2D) representations of virtual resections enabling the visualization of information associated with the surgical plan.

Methods:

Resectograms are computed in real-time and displayed as additional 2D views showing anatomical, functional, and risk-associated information extracted from the 3D virtual resection as this is modified during planning, offering surgeons an occlusion-free visualization of the virtual resection during surgery planning. To further improve functionality, we explored three flattening methods: fixed-shape, Least Squares Conformal Maps, and As-Rigid-As-Possible, to generate these 2D views. Additionally, we optimized GPU memory usage by downsampling texture objects, ensuring errors remain within acceptable limits as defined by surgeons.

Results:

We evaluated Resectograms with experienced surgeons (n = 4, 9-15 years) and assessed 2D flattening methods with computer and biomedical scientists (n = 11) through visual experiments. Surgeons found Resectograms valuable for enhancing surgical planning effectiveness and accuracy. Among flattening methods, Least Squares Conformal Maps and As-Rigid-As-Possible techniques demonstrated similarly low distortion levels, superior to the fixed-shape approach. Our analysis of texture object downsampling revealed effectiveness for liver and tumor segmentations, but less so for vessel segmentations.

Conclusions:

This paper presents Resectograms, a novel method for visualizing liver virtual resection plans in 2D, offering an intuitive, occlusion-free representation computable in real-time. Resectograms incorporate multiple information layers, providing comprehensive data for liver surgery planning. We enhanced the visualization through improved 3D-to-2D orientation mapping and distortion-minimizing parameterization algorithms. This research contributes to advancing liver surgery planning tools by offering a more accessible and informative visualization method. The code repository for this work is available at: https://github.com/ALive-research/Slicer-Liver.

背景与目的：虚拟切除的可视化在计算机辅助肝脏手术计划中起着核心作用。然而，复杂的肝脏解剖信息往往导致闭塞和可视化信息混乱，从而导致虚拟切除的不准确性。为了克服这些挑战，我们引入了切除图，它是虚拟切除的平面（2D）表示，使与手术计划相关的信息可视化。方法：实时计算切除图，并显示为额外的2D视图，显示从3D虚拟切除中提取的解剖，功能和风险相关信息，因为在计划期间对其进行了修改，为外科医生在手术计划期间提供无闭塞的虚拟切除可视化。为了进一步改进功能，我们探索了三种扁平化方法：固定形状、最小二乘共形映射和尽可能刚性映射，以生成这些2D视图。此外，我们通过降低纹理对象的采样来优化GPU内存使用，确保误差保持在外科医生定义的可接受范围内。结果：我们与经验丰富的外科医生（n = 4, 9-15年）评估了Resectograms，并与计算机和生物医学科学家（n = 11）通过视觉实验评估了二维平坦化方法。外科医生发现切除图对提高手术计划的有效性和准确性很有价值。在平坦化方法中，最小二乘共形映射和尽可能刚性技术显示出类似的低失真水平，优于固定形状方法。我们对纹理对象下采样的分析显示了对肝脏和肿瘤分割的有效性，但对血管分割的效果较差。结论：本文提出了一种新的方法，用于在2D中可视化肝脏虚拟切除计划，提供直观，无阻塞的实时计算表示。切除图包含多个信息层，为肝脏手术计划提供全面的数据。我们通过改进的3d到2d方向映射和最小化扭曲参数化算法增强了可视化。本研究通过提供一种更方便和信息丰富的可视化方法，有助于推进肝脏手术计划工具。这项工作的代码存储库可从：https://github.com/ALive-research/Slicer-Liver获得。

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引用次数: 0

GLAAM and GLAAI: Pioneering attention models for robust automated cataract detection GLAAM和GLAAI：用于稳健自动白内障检测的开创性注意力模型

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100182

Deepak Kumar , Chaman Verma , Zoltán Illés

Background and Objective:

Early detection of eye diseases, especially cataracts, is essential for preventing vision impairment. Accurate and cost-effective cataract diagnosis often requires advanced methods. This study proposes novel deep learning models that integrate global and local attention mechanisms into MobileNet and InceptionV3 architectures to improve cataract detection from fundus images.

Methods:

Two deep learning models, Global–Local Attention Augmented MobileNet (GLAAM) and Global–Local Attention Augmented InceptionV3 (GLAAI), were developed to enhance the analysis of fundus images. The models incorporate a combined attention mechanism to effectively capture deteriorated regions in retinal images. Data augmentation techniques were employed to prevent overfitting during training and testing on two cataract datasets. Additionally, Grad-CAM visualizations were used to increase interpretability by highlighting key regions influencing predictions.

Results:

The GLAAM model achieved a balanced accuracy of 97.08%, an average precision of 97.11%, and an F1-score of 97.12% on the retinal dataset. Grad-CAM visualizations confirmed the models’ ability to identify crucial cataract-related regions in fundus images.

Conclusion:

This study demonstrates a significant advancement in cataract diagnosis using deep learning, with GLAAM and GLAAI models exhibiting strong diagnostic performance. These models have the potential to enhance diagnostic tools and improve patient care by offering a cost-effective and accurate solution for cataract detection, suitable for integration into clinical settings.

背景和目的：早期发现眼疾，尤其是白内障，对于预防视力损伤至关重要。准确且经济高效的白内障诊断通常需要先进的方法。本研究提出了新颖的深度学习模型，将全局和局部注意力机制整合到 MobileNet 和 InceptionV3 架构中，以改进眼底图像的白内障检测。这些模型结合了联合注意力机制，可有效捕捉视网膜图像中的恶化区域。在两个白内障数据集的训练和测试过程中，采用了数据增强技术来防止过度拟合。结果：在视网膜数据集上，GLAAM 模型的均衡准确率达到了 97.08%，平均精确率达到了 97.11%，F1 分数达到了 97.12%。Grad-CAM 可视化证实了模型识别眼底图像中关键白内障相关区域的能力。这些模型具有增强诊断工具和改善患者护理的潜力，可为白内障检测提供具有成本效益的准确解决方案，适合集成到临床环境中。

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引用次数: 0

Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100191

Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

Background

The issue is that most heart attacks and strokes happen unexpectedly to people who have signs of high blood pressure that are not identified in time for treatment. These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen.

Methods

This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. This database comprises 1200 retinal images, each measuring 1440 × 940 pixels. The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). This stage aims to extract and sample the comparison between the width of the artery and vein (AVR). This research uses a Deep Convolutional Neural Network (DCNN) classification model with cross-validation training using the Leave-one-out method.

Results

The results of testing the model with nine output classes, the features extracted in each convolutional layer, the second layer successfully extracts the retina and eye blood vessels, the third layer extracts the retinal image texture, and the fourth layer extracts hard exudates, hemorrhages, and cotton wool spots. Meanwhile, the Specificity, Recall, Accuracy, and F-Score results are 90 %, 81.82 %, 90 %, and 90 %, respectively.

Conclusions

This research's findings first include applying the AVR calculation algorithm to build a new dataset with 9 class categories. Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.

问题是，大多数心脏病发作和中风意外发生在那些有高血压症状的人身上，而这些症状没有及时发现并进行治疗。这些空白因素使得对高血压视网膜病变的研究迫在眉睫，因为它需要一个早期发现模型来提高治疗的准确性，并在心脏病发作和中风发生之前进行预防。方法本研究利用二手数据，特别是来自开源Messidor数据库的视网膜图像数据集。该数据库包含1200张视网膜图像，每张图像的尺寸为1440 × 940像素。数据集分为60%的训练数据和40%的验证数据。下一步是图像分析过程，其中包括使用Otsu分割算法提取视网膜血管。形态学方法用于获得视盘周围血管的综合特征。这一阶段的目的是提取和采样对比的动脉和静脉的宽度（AVR）。本研究使用深度卷积神经网络（DCNN）分类模型，并使用留一法进行交叉验证训练。结果对模型进行了9个输出类的测试，每个卷积层提取的特征，第二层成功提取视网膜和眼部血管，第三层提取视网膜图像纹理，第四层提取硬渗出物、出血物和棉絮斑。特异性为90%，召回率为81.82%，准确率为90%，F-Score为90%。本研究的发现首先包括应用AVR计算算法构建一个包含9个类的新数据集。其次，确定CNN模型的体系结构规范，通过调整超参数设置每层的输入大小、深度和节点数，以及传递函数、学习率和epoch数。

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引用次数: 0

Acoustic cues for person identification using cough sounds 用咳嗽声作为识别人的声学线索

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100195

Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

Objectives

This study presents an improved approach to person identification (PID) using nonverbal vocalizations, focusing specifically on cough sounds as a biometric modality. While recent works have demonstrated the feasibility of cough-based PID (CPID), most report accuracies around 80–90 % and could face limitations in terms of model efficiency, generalization, or robustness. Our objective is to advance CPID performance through compact model design and more effective training strategies.

Methods

We collected a custom dataset from 19 subjects and developed a lightweight yet effective deep learning framework for CPID. The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. The training process incorporates a hybrid loss function that combines supervised contrastive (SC) learning and cross-entropy (CE) loss to enhance feature discrimination. We systematically evaluated multiple acoustic representations, including MFCCs and spectrograms, to identify the most suitable features. We also applied data augmentation for robustness and investigated cross-modal transferability by testing speech-trained models on cough data.

Results

Our CPID system achieved a mean identification accuracy of 97.18 %. Training the proposed CoughCueNet using a hybrid SC+CE loss function consistently improved model generalization and robustness. It outperformed the same network and larger-capacity networks (i.e., VGG16 and ResNet50) trained with CE loss alone, which achieved accuracies around 90 %. Among the tested features, MFCCs yielded superior identification performance over spectrograms. Experiments with speech-trained models tested on coughs revealed limited cross-vocal transferability, emphasizing the need for cough-specific models.

Conclusion

This work advances the state of cough-based PID by demonstrating that high-accuracy identification is achievable using compact models and hybrid training strategies. It establishes cough sounds as a practical and distinctive biometric modality, with promising applications in security, user authentication, and health monitoring, particularly in environments where speech-based systems are less reliable or infeasible.

目的：本研究提出了一种改进的非语言发声方法来识别人（PID），特别关注咳嗽声音作为一种生物识别模式。虽然最近的研究已经证明了基于咳嗽的PID （CPID）的可行性，但大多数报告的准确率约为80 - 90%，并且在模型效率、泛化或鲁棒性方面可能面临限制。我们的目标是通过紧凑的模型设计和更有效的培训策略来提高CPID的表现。方法我们收集了来自19名受试者的自定义数据集，并开发了一个轻量级但有效的CPID深度学习框架。提出的架构CoughCueNet是一个卷积循环神经网络，旨在捕捉咳嗽声音的空间和时间模式。训练过程结合了一个混合损失函数，该函数结合了监督对比（SC）学习和交叉熵（CE）损失来增强特征识别。我们系统地评估了多种声学表征，包括mfc和频谱图，以确定最合适的特征。我们还应用数据增强来增强鲁棒性，并通过测试咳嗽数据上的语音训练模型来研究跨模态可移植性。结果CPID系统的平均识别准确率为97.18%。使用混合SC+CE损失函数训练所提出的CoughCueNet，不断提高模型的泛化和鲁棒性。它优于单独使用CE损失训练的相同网络和更大容量网络（即VGG16和ResNet50），其准确率达到90%左右。在测试的特征中，MFCCs产生了优于谱图的识别性能。用经过语言训练的模型对咳嗽进行的实验显示，跨声音可移植性有限，这强调了对特定咳嗽模型的需求。本研究通过证明使用紧凑模型和混合训练策略可以实现高精度的识别，从而提高了基于咳嗽的PID的状态。它将咳嗽声音确立为一种实用而独特的生物识别方式，在安全、用户身份验证和健康监测方面具有很好的应用前景，特别是在基于语音的系统不太可靠或不可行的环境中。

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引用次数: 0

Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks 基于改进U-Net的胸部x线图像鲁棒肺分割

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100211

Wiley Tam , Paul Babyn , Javad Alirezaie

Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in delayed diagnosis and treatment. In response, Computer-Aided Diagnostic (CAD) tools can be used to potentially streamline and expedite the diagnostic process. Recently, deep learning techniques have gained prominence in the automated analysis of CXR images, particularly in segmenting lung regions as a critical preliminary step. This study aims to develop and evaluate a lung segmentation model based on a modified U-Net architecture. The architecture leverages techniques such as transfer learning with DenseNet201 as a feature extractor alongside dilated convolutions and residual blocks. An ablation study was conducted to evaluate these architectural components, along with additional elements like augmented data, alternative backbones, and attention mechanisms. Numerous and extensive experiments were performed on two publicly available datasets, the Montgomery County (MC) and Shenzhen Hospital (SH) datasets, to validate the efficacy of these techniques on segmentation performance. Outperforming other state-of-the-art methods on the MC dataset, the proposed model achieved a Jaccard Index (IoU) of 97.77 and a Dice Similarity Coefficient (DSC) of 98.87. These results represent a significant improvement over the baseline U-Net, with gains of 3.37% and 1.75% in IoU and DSC, respectively. These findings highlight the importance of architectural enhancements in deep learning-based lung segmentation models, contributing to more efficient, accurate, and reliable CAD systems for lung disease assessment.

世界卫生组织（世卫组织）的统计数据证明，肺部疾病仍然是全世界死亡的主要原因。放射科医生解释胸部x光片（CXR）图像以诊断常见肺部疾病的有限可用性构成了重大挑战，经常导致诊断和治疗延迟。因此，可以使用计算机辅助诊断（CAD）工具来简化和加快诊断过程。最近，深度学习技术在CXR图像的自动分析中获得了突出的地位，特别是在分割肺区域作为关键的初步步骤方面。本研究旨在开发和评估一个基于改进U-Net架构的肺分割模型。该架构利用了DenseNet201的迁移学习等技术，作为扩展卷积和残差块的特征提取器。我们进行了一项消融研究，以评估这些架构组件，以及其他元素，如增强数据、替代主干和注意机制。在蒙哥马利县（MC）和深圳医院（SH）两个公开可用的数据集上进行了大量广泛的实验，以验证这些技术在分割性能方面的有效性。该模型在MC数据集上的表现优于其他最先进的方法，其Jaccard Index （IoU）为97.77,Dice Similarity Coefficient （DSC）为98.87。这些结果表明，与基线U-Net相比，IoU和DSC分别增加了3.37%和1.75%。这些发现强调了基于深度学习的肺分割模型的架构增强的重要性，有助于更有效、准确和可靠的肺部疾病评估CAD系统。

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