Intelligence-based medicine最新文献_第3页

Diffusion models vs. DCGANs for class-imbalanced lung cancer CT classification: A comparative study 扩散模型与dcgan在分级不平衡肺癌CT分型中的比较研究

Intelligence-based medicine

Pub Date : 2025-12-22 DOI: 10.1016/j.ibmed.2025.100336

Masoud Tabibian, Tahereh Razmpour, Rajib Saha

Effective lung cancer detection from CT scans remains critically challenged by class imbalance where benign and normal cases are underrepresented, leading to biased machine learning models with reduced sensitivity for minority classes and potentially missed diagnoses in cancer screening applications. We present a comprehensive comparative analysis of Diffusion Models and Deep Convolutional Generative Adversarial Networks (DCGANs), both incorporating modern architectural enhancements including spectral normalization, self-attention mechanisms, and conditional generation, for addressing class imbalance in lung cancer CT classification. Using the IQ-OTH/NCCD dataset comprising 1097 CT images across normal, benign, and malignant categories with statistical validation across 10 independent runs, we evaluated both approaches through quantitative image quality metrics (Fréchet Inception Distance, Kullback-Leibler divergence, Kernel Inception Distance, and Inception Score) and downstream classification performance. While Diffusion models consistently outperformed DCGANs across most image quality measures, the clinical significance was confirmed through task-based validation. Both generative approaches successfully addressed class imbalance: DCGAN-augmented datasets achieved overall accuracy of 0.9760 ± 0.0116 with benign recall improvement from 0.833 to 0.933, while Diffusion-augmented datasets reached superior performance of 0.9959 ± 0.0068 with perfect benign recall (1.000 ± 0.000). Critically for cancer screening where false negatives carry severe consequences, Diffusion maintained the highest malignant detection sensitivity (0.997 ± 0.008) with substantially lower performance variance, demonstrating more consistent synthetic data quality. These findings establish that while both modern architectures can mitigate class imbalance, Diffusion models' superior recall performance and lower variability position them as the preferred approach for high-stakes clinical applications, demonstrating that ultimate validation must prioritize downstream clinical task performance over image quality metrics alone.

CT扫描中肺癌的有效检测仍然受到类别不平衡的严重挑战，其中良性和正常病例的代表性不足，导致机器学习模型对少数类别的敏感性降低，并可能在癌症筛查应用中遗漏诊断。我们对扩散模型和深度卷积生成对抗网络（dcgan）进行了全面的比较分析，两者都采用了现代架构增强，包括谱归一化、自注意机制和条件生成，以解决肺癌CT分类中的类别不平衡问题。使用IQ-OTH/NCCD数据集，包括1097张CT图像，包括正常、良性和恶性类别，并在10次独立运行中进行统计验证，我们通过定量图像质量指标（fr起始距离、Kullback-Leibler散度、核起始距离和起始分数）和下游分类性能来评估这两种方法。虽然扩散模型在大多数图像质量测量中始终优于dcgan，但通过基于任务的验证证实了临床意义。两种生成方法都成功地解决了类不平衡问题：dcgan增强数据集的总体准确率为0.9760±0.0116，良性召回率从0.833提高到0.933，而扩散增强数据集的总体准确率为0.9959±0.0068，良性召回率为1.000±0.000。对于假阴性会带来严重后果的癌症筛查来说，Diffusion保持了最高的恶性检测灵敏度（0.997±0.008），性能差异显著降低，显示出更一致的合成数据质量。这些发现表明，虽然这两种现代架构都可以缓解类失衡，但扩散模型优越的召回性能和较低的可变性使其成为高风险临床应用的首选方法，这表明最终验证必须优先考虑下游临床任务性能，而不是单独的图像质量指标。

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引用次数: 0

Cardiac Magnetic Resonance-to-Computed Tomography Angiography image conversion using diffusion models for Transcatheter Aortic Valve Implantation planning 利用扩散模型对经导管主动脉瓣植入计划进行心脏磁共振-计算机断层血管造影图像转换

Intelligence-based medicine

Pub Date : 2025-12-19 DOI: 10.1016/j.ibmed.2025.100335

Carmen Guadalupe Colin-Tenorio , Agnes Mayr , Christian Kremser , Markus Haltmeier , Enrique Almar-Munoz

Introduction:

Transcatheter Aortic Valve Implantation (TAVI) has become the preferred method for treating severe aortic stenosis, especially in patients who are unsuitable for traditional surgery. Typically, preoperative imaging for TAVI involves contrast-enhanced Computed Tomography Angiography (CTA). However, for patients with contraindications to contrast agents, Cardiac Magnetic Resonance imaging (CMR) is a viable alternative, albeit with its limitations in visualizing calcifications.

Methods:

This study explores the application of diffusion models to enhance CMR-to-CTA contrast-free image conversion, to avoid the use of contrast agents and ionizing radiation. We developed a pipeline incorporating Denoising Diffusion Probabilistic Models (DDPMs) and Stochastic Differential Equations (SDE) models to synthesize CTA-equivalent images from CMR scans. We evaluated this approach using an in-house dataset consisting of 39 paired CTA and CMR scans. For the training process, coregistration of both modalities was required, which we achieved by performing rigid registration using the segmented aorta masks.

Results:

Our results show that the synthesized CTA images maintain high fidelity to the actual scans. This is quantitatively supported by a mean Structural Similarity Index Measure (SSIM) of 0.8 and a Peak Signal-to-Noise Ratio (PSNR) of 22 dB using conditional Stochastic Differential Equations (SDE) and Prediction-Correction (PC), indicating strong structural preservation and low reconstruction error. However, the model occasionally fails to accurately detect valve calcifications, likely due to limitations in capturing subtle pathological details that are not visually discernible in CMR images.

Conclusion:

Diffusion models used to synthesize CTA images from CMR datasets achieve high accuracy, providing a contrast-free alternative for TAVI planning and potential insights into valvular calcification patterns. However, accurate visualization of valve calcification occasionally fails, and larger datasets are desirable for validation.

导论：经导管主动脉瓣植入术（Transcatheter Aortic Valve Implantation， TAVI）已成为治疗严重主动脉瓣狭窄的首选方法，特别是对于不适合传统手术治疗的患者。通常情况下，TAVI的术前成像包括对比增强计算机断层血管造影（CTA）。然而，对于有造影剂禁忌症的患者，心脏磁共振成像（CMR）是一种可行的替代方案，尽管在可视化钙化方面存在局限性。方法：本研究探索应用扩散模型增强cmr - cta无对比图像转换，避免使用造影剂和电离辐射。我们开发了一个结合去噪扩散概率模型（ddpm）和随机微分方程（SDE）模型的管道，以合成CMR扫描的cta等效图像。我们使用由39对CTA和CMR扫描组成的内部数据集来评估这种方法。对于训练过程，需要两种模式的共同注册，我们通过使用分段主动脉面罩进行刚性注册来实现。结果：合成的CTA图像与实际扫描保持了较高的保真度。基于条件随机微分方程（SDE）和预测校正（PC）的平均结构相似指数（SSIM）为0.8，峰值信噪比（PSNR）为22 dB，这在定量上支持了这一结论，表明结构保存性强，重建误差低。然而，该模型偶尔不能准确地检测到瓣膜钙化，这可能是由于在捕捉CMR图像中无法视觉识别的细微病理细节方面的限制。结论：用于从CMR数据集合成CTA图像的扩散模型具有较高的准确性，为TAVI规划提供了无对比度的替代方案，并可能深入了解瓣膜钙化模式。然而，瓣膜钙化的精确可视化有时会失败，需要更大的数据集进行验证。

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引用次数: 0

Automated detection of HER2 gene copy number in breast cancer using deep learning techniques 利用深度学习技术自动检测乳腺癌中HER2基因拷贝数

Intelligence-based medicine

Pub Date : 2025-12-18 DOI: 10.1016/j.ibmed.2025.100333

Terisara Micaraseth , Shanop Shuangshoti , Sakdina Prommaouan , Somruetai Shuangshoti , Rizwan Ullah , Gridsada Phanomchoeng

Accurate evaluation of HER2 gene amplification is critical for guiding breast cancer treatment decisions. This study proposes a deep learning-based diagnostic system for analyzing Dual In Situ Hybridization (DISH) images to support HER2 status assessment. The system integrates two models— YOLOv11-seg for cell detection and YOLOv11 object detection models for HER2 and CEP17 signal quantification—into a unified pipeline. High-resolution whole-slide images were preprocessed and annotated to train the models, which were then embedded into a standalone application designed for clinical environments. Upon uploading TIFF format images, the application performs automated cell detection, red/black signal analysis, and HER2/CEP17 ratio computation. Experimental results demonstrated an accuracy 95.24 % for the best identification and mean deviations of 6.08 % (CEP17) and 12.78 % (HER2) compared with manual counting. Statistical analyses confirm high consistency, particularly in red signal detection. Clinical feedback under scores the system's ease of use, accuracy, and potential to reduce diagnostic burden. The proposed approach demonstrates strong feasibility for routine adoption in pathology workflows.

准确评估HER2基因扩增对指导乳腺癌治疗决策至关重要。本研究提出了一种基于深度学习的诊断系统，用于分析双原位杂交（DISH）图像，以支持HER2状态评估。该系统将用于细胞检测的YOLOv11-seg和用于HER2和CEP17信号量化的YOLOv11目标检测模型集成到一个统一的管道中。对高分辨率的整张幻灯片图像进行预处理和注释以训练模型，然后将其嵌入为临床环境设计的独立应用程序中。在上传TIFF格式的图像后，应用程序执行自动细胞检测，红/黑信号分析和HER2/CEP17比率计算。实验结果表明，与人工计数相比，该方法的最佳识别准确率为95.24%，平均偏差为6.08% （CEP17）和12.78% （HER2）。统计分析证实了高一致性，特别是在红色信号检测方面。临床反馈评分系统的易用性、准确性和减少诊断负担的潜力。所提出的方法证明了在病理工作流程中常规采用的强大可行性。

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引用次数: 0

Optimization framework for overcoming tyrosine kinase inhibitor resistance: Multi-objective selection, scheduling, and adaptive therapy 克服酪氨酸激酶抑制剂耐药性的优化框架：多目标选择、调度和适应性治疗

Intelligence-based medicine

Pub Date : 2025-12-18 DOI: 10.1016/j.ibmed.2025.100332

Mohanad A. Deif , Mohamed A. Hafez , Mohammad Khishe

Tyrosine kinase inhibitors are key drugs in targeted cancer therapy but often fail when resistance emerges. Many predictive methods focus on accuracy alone, while calibration and kinase selectivity, which matter for clinical use, receive less attention. We present a single framework that treats resistance prediction and dosing decisions as a three-objective problem: minimize misclassification, reduce calibration error, and increase selectivity. Using calibrated probabilities and tuned thresholds, baseline models improved in ROC–AUC and expected calibration error across stratified, scaffold, and mutation-cold splits. Pareto analysis with hypervolume and coverage showed that including selectivity changes the relative ranking of inhibitors and exposes trade-offs that accuracy alone cannot capture. On the treatment side, we compared continuous dosing, hysteresis switching, and adaptive model predictive control in a two-compartment tumor model. Adaptive control lowered total dose by about 18% and extended simulated survival by more than 25 weeks. These results provide a clear proof of concept that combining machine learning, multi-objective optimization, and adaptive therapy can improve prediction quality and guide personalized dosing to better manage resistance.

酪氨酸激酶抑制剂是靶向癌症治疗的关键药物，但往往在出现耐药性时失效。许多预测方法只关注准确性，而校准和激酶选择性对临床使用很重要，却很少受到关注。我们提出了一个单一的框架，将耐药性预测和给药决策视为一个三目标问题：最大限度地减少错误分类，减少校准误差，增加选择性。使用校准概率和调整阈值，基线模型改进了ROC-AUC和分层、支架和突变冷分裂的预期校准误差。使用超容量和覆盖的帕累托分析表明，包括选择性改变了抑制剂的相对排名，并暴露了仅靠准确性无法捕获的权衡。在治疗方面，我们比较了两室肿瘤模型中的连续给药、迟滞开关和自适应模型预测控制。适应性控制将总剂量降低了18%，并将模拟生存期延长了25周以上。这些结果提供了一个清晰的概念证明，结合机器学习、多目标优化和自适应治疗可以提高预测质量，并指导个性化给药，以更好地管理耐药性。

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引用次数: 0

Contrasting deep learning audio models for direct respiratory insufficiency detection versus blood oxygen saturation estimation 对比深度学习音频模型用于直接呼吸功能不全检测与血氧饱和度估计

Intelligence-based medicine

Pub Date : 2025-12-12 DOI: 10.1016/j.ibmed.2025.100331

Marcelo Matheus Gauy , Natália Hitomi Koza , Ricardo Mikio Morita , Gabriel Rocha Stanzione , Arnaldo Cândido Júnior , Larissa Cristina Berti , Anna Sara Shafferman Levin , Ester Cerdeira Sabino , Flaviane Romani Fernandes Svartman , Marcelo Finger

This work aims to investigate the strengths and limitations of non-invasive audio-based deep learning methods for the detection of respiratory conditions. We contrast the performance obtained in tasks such as the expert-centered respiratory insufficiency (RI) detection with easily measured blood oxygen saturation (SpO2) estimation. Several deep learning audio models have been recently proposed for RI detection via voice and speech analysis; these models have obtained an accuracy of 95% in general patients and 97.4% in COVID-19 patients. Here, we extend those results, refining several pretrained audio neural networks (CNN6, CNN10 and CNN14) and Masked Autoencoders (Audio-MAE) for RI detection, showing that some of these models achieve near perfect accuracy (99.9% on COVID RI and 98.6% on general RI). The models were pretrained on AudioSet resulting in improved performance, with transfer learning playing a key role in the prevention of overfitting. The near-perfect RI detection performance suggests that low-cost and automated methods could be developed for assisting patient triage. In parallel, this paper seeks to verify SpO2 estimation feasibility, so we perform a 92% SpO2-threshold binary classification using the same architectures. In contrast to our findings for RI, this model yielded an accuracy below 70% and MCC-correlation below 0.3, indicating both that SpO2 estimation solely from audio is unfeasible and the presence of multiple features in the audios which are useful for RI detection, but not for SpO2 estimation. We propose that this discrepancy demonstrates the limits of voice and speech biomarkers across different diagnostic tasks under current technologies.

这项工作旨在研究非侵入性基于音频的深度学习方法用于检测呼吸系统疾病的优势和局限性。我们将以专家为中心的呼吸功能不全（RI）检测与易于测量的血氧饱和度（SpO2）估计等任务中的性能进行了对比。最近提出了几个深度学习音频模型，用于通过语音和语音分析进行RI检测；这些模型在普通患者中的准确率为95%，在COVID-19患者中的准确率为97.4%。在这里，我们扩展了这些结果，改进了几个预训练的音频神经网络（CNN6， CNN10和CNN14）和掩码自动编码器（audio - mae）用于RI检测，表明其中一些模型达到了近乎完美的精度（COVID RI为99.9%，普通RI为98.6%）。在AudioSet上对模型进行预训练，从而提高了性能，迁移学习在防止过拟合方面发挥了关键作用。近乎完美的RI检测性能表明，可以开发低成本和自动化的方法来协助患者分诊。同时，本文试图验证SpO2估计的可行性，因此我们使用相同的架构执行92%的SpO2阈值二值分类。与我们的研究结果相比，该模型的RI精度低于70%，mcc相关性低于0.3，这表明仅从音频中估计SpO2是不可实现的，并且音频中存在多个特征，这些特征对RI检测有用，但对SpO2估计无效。我们认为，这种差异表明在当前技术下，语音和语音生物标志物在不同诊断任务中的局限性。

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引用次数: 0

Hybrid spatiotemporal feature fusion for robust lesion detection and tracking in breast ultrasound video data 基于混合时空特征融合的乳腺超声视频数据鲁棒病灶检测与跟踪

Intelligence-based medicine

Pub Date : 2025-12-10 DOI: 10.1016/j.ibmed.2025.100330

Radwan Qasrawi , Suliman Thwib , Ghada Issa , Razan AbuGhoush , Hussein AlMasri , Marah Qawasmi , Nael Abu Halaweh

Background

Speckle noise, tissue deformation, low contrast, and frame inconsistencies limit the reliability of traditional breast lesion tracking approaches in ultrasound videos.

Objective

This study aims to develop a robust hybrid framework that integrates advanced image enhancement, deep learning-based detection, and spatiotemporal feature fusion for improved lesion detection and tracking in breast ultrasound video sequences.

Methods

We propose a two-phase computational framework. The first phase employs Contrast-Limited Adaptive Histogram Equalization (CLAHE) for local contrast enhancement, followed by a hybrid denoising strategy combining anisotropic diffusion and unsharp masking to suppress noise and preserve edge sharpness. In the second phase, lesion detection is performed using a YOLOv11-L model, fine-tuned on a curated dataset of annotated breast ultrasound images. For tracking, we utilize Kernelized Correlation Filtering (KCF) enhanced with a Hybrid Spatiotemporal Context (STC) representation. The system is evaluated on a dataset comprising 11,382 ultrasound images and 40 video sequences, with performance assessed using Intersection over Union (IoU), success rate, failure rate, and processing speed.

Results

The proposed framework achieved an IoU of 0.878 for benign lesions and 0.881 for malignant lesions. The integration of STC features and YOLO detection reduced tracking failure rates by over 25 % and improved success rates to 99.0 % for benign and 99.4 % for malignant lesions. The system processed 41–45 frames per second in real time.

Conclusions

Our framework provides an effective solution for real-time lesion detection and tracking in breast ultrasound videos. By enhancing both accuracy and reliability, it supports improved clinical decision-making in breast cancer diagnostics.

斑点噪声、组织变形、低对比度和帧不一致限制了超声视频中传统乳腺病变跟踪方法的可靠性。本研究旨在开发一个强大的混合框架，将先进的图像增强、基于深度学习的检测和时空特征融合相结合，以改进乳腺超声视频序列的病变检测和跟踪。方法提出了一种两阶段计算框架。第一阶段采用对比度限制自适应直方图均衡化（CLAHE）进行局部对比度增强，然后采用各向异性扩散和非锐利掩蔽相结合的混合降噪策略来抑制噪声并保持边缘清晰度。在第二阶段，使用YOLOv11-L模型进行病变检测，并在精心设计的带注释的乳腺超声图像数据集上进行微调。为了跟踪，我们使用了混合时空上下文（STC）表示增强的核化相关滤波（KCF）。该系统在包含11,382张超声图像和40个视频序列的数据集上进行了评估，并使用交汇交汇（IoU）、成功率、故障率和处理速度对性能进行了评估。结果该框架良性病变IoU为0.878，恶性病变IoU为0.881。STC特征与YOLO检测的结合使跟踪失败率降低了25%以上，良性病变的成功率提高到99.0%，恶性病变的成功率提高到99.4%。系统实时处理41-45帧/秒。结论sour框架为乳腺超声视频中病灶的实时检测和跟踪提供了有效的解决方案。通过提高准确性和可靠性，它支持改善乳腺癌诊断的临床决策。

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引用次数: 0

A machine learning–based method for supporting the diagnosis of eosinophilic granulomatosis with polyangiitis: Development and evaluation 一种基于机器学习的支持诊断嗜酸性肉芽肿病合并多血管炎的方法：发展和评估

Intelligence-based medicine

Pub Date : 2025-11-28 DOI: 10.1016/j.ibmed.2025.100317

Yosra Bouattour , Mohamed Hédi Maâloul , Zouhir Bahloul , Sameh Marzouk

Background/objectives

Eosinophilic granulomatosis with polyangiitis (EGPA), formerly Churg-Strauss syndrome, is a rare systemic vasculitis often diagnosed late due to its heterogeneous presentation, leading to severe complications—particularly cardiac involvement, a major cause of morbidity and mortality. We developed EGPA-ML, an artificial intelligence (AI)-based tool using supervised machine learning (ML), to support early and accurate EGPA diagnosis, especially in non-specialized settings.

Methods

A retrospective cohort of patients evaluated for suspected vasculitis at Hedi Chaker Hospital, Sfax, Tunisia, from 1997 to 2023 (nearly three decades), provided 1904 clinical, biological, and histological features. After data cleaning, standardization, and feature selection, 56 key features were retained. Patients were classified as {EGPA} or {NOT_EGPA} per the 2022 ACR/EULAR criteria, with expert consensus (κ = 0.85). Multiple supervised ML algorithms were evaluated via 10-fold cross-validation. The best model was integrated into EGPA-ML, a Java-based clinical decision support system. Performance was assessed on an independent dataset of n = 280 key features, with reference classification {EGPA}/{NOT_EGPA} validated by experts (κ = 0.89).

Results

On the test and evaluation dataset, EGPA-ML achieved a recall of 0.992, precision of 0.869, and F1-score of 0.926. Feature importance analysis identified asthma and eosinophil count as top predictors (36.5 % each), followed by ANCA status, vascular purpura, and histological vasculitis.

Conclusions

EGPA-ML is a high-performance, interpretable, and adaptive tool based on supervised ML, supporting timely EGPA diagnosis. It represents a practical advancement for clinical decision-making in rare diseases, particularly in internal medicine, pulmonology, and cardiology.

背景/目的嗜酸性肉芽肿病合并多血管炎（EGPA），以前称为Churg-Strauss综合征，是一种罕见的全身性血管炎，由于其异质表现，通常诊断较晚，导致严重的并发症，特别是心脏受累，是发病率和死亡率的主要原因。我们开发了EGPA-ML，这是一种基于人工智能（AI）的工具，使用监督机器学习（ML）来支持早期和准确的EGPA诊断，特别是在非专业环境中。方法对1997年至2023年（近30年）在突尼斯斯法克斯Hedi Chaker医院接受疑似血管炎评估的患者进行回顾性队列分析，提供了1904项临床、生物学和组织学特征。经过数据清理、标准化和特征选择，保留了56个关键特征。根据2022年ACR/EULAR标准将患者分为{EGPA}或{NOT_EGPA}，专家共识（κ = 0.85）。通过10倍交叉验证评估多个监督ML算法。将最佳模型集成到基于java的临床决策支持系统EGPA-ML中。在n = 280个关键特征的独立数据集上进行性能评估，参考分类{EGPA}/{NOT_EGPA}经过专家验证（κ = 0.89）。结果在测试和评价数据集中，EGPA-ML的召回率为0.992，精密度为0.869，f1得分为0.926。特征重要性分析发现哮喘和嗜酸性粒细胞计数是最重要的预测因子（各占36.5%），其次是ANCA状态、血管性紫癜和组织学血管炎。结论segpa -ML是一种基于监督式ML的高性能、可解释性和自适应的工具，支持EGPA的及时诊断。它代表了罕见病临床决策的实际进步，特别是在内科、肺脏学和心脏病学方面。

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引用次数: 0

Radiological trends in convolutional neural networks for breast cancer diagnosis 卷积神经网络在乳腺癌诊断中的放射学趋势

Intelligence-based medicine

Pub Date : 2025-11-27 DOI: 10.1016/j.ibmed.2025.100322

Ka Lee Li , Martin Ga Zen Tam , Sai Ka Li , Fatema Aftab

Breast cancer remains a leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for patient outcomes. Future diagnosis may employ convolutional neural networks (CNNs), which have established themselves as powerful multi-layered artificial intelligence (AI) tools for computer vision tasks, with growing applications in breast cancer detection, diagnosis and classification. To provide insight into this field's intellectual, social, and conceptual knowledge structures, we conducted a bibliometric review of its 100 most-cited articles. The review looked at articles from January 1, 1995 to August 23, 2024. Our network analyses encourage increased inter-country collaboration. Thematic mapping highlights the increasing role of CNNs as foundational components in present and future AI applications. Multiple correspondence analyses track progress in diagnostic accuracy, system performance, and advanced classification techniques. Study design analyses suggest a need for future CNN research to be benchmarked against human readers and foster closer collaboration between technical and clinical researchers. In this bibliometric analysis, we summarise key contributions, examine emerging research trends, and provide an overview of the evolving landscape of CNN applications in breast cancer diagnostics.

乳腺癌仍然是世界范围内癌症相关死亡的主要原因，因此早期和准确的诊断对患者的预后至关重要。未来的诊断可能会使用卷积神经网络（cnn），卷积神经网络已经成为计算机视觉任务中强大的多层人工智能（AI）工具，在乳腺癌检测、诊断和分类方面的应用越来越多。为了深入了解这个领域的智力、社会和概念知识结构，我们对100篇被引用最多的文章进行了文献计量分析。该评论研究了1995年1月1日至2024年8月23日的文章。我们的网络分析鼓励加强国家间合作。专题映射强调了cnn在当前和未来人工智能应用中作为基础组件的日益重要的作用。多个对应分析跟踪诊断准确性、系统性能和高级分类技术的进展。研究设计分析表明，未来的CNN研究需要以人类读者为基准，并促进技术和临床研究人员之间更密切的合作。在这篇文献计量分析中，我们总结了主要贡献，研究了新兴的研究趋势，并概述了CNN在乳腺癌诊断中应用的发展前景。

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引用次数: 0

Capsule-augmented deep learning architectures for mental health detection from social media text 用于社交媒体文本心理健康检测的胶囊增强深度学习架构

Intelligence-based medicine

Pub Date : 2025-11-24 DOI: 10.1016/j.ibmed.2025.100319

Faheem Ahmad Wagay, Jahiruddin

Mental health detection from social media text has attracted growing research attention due to the global rise in mental health concerns. Traditional deep learning models, such as Bidirectional Long Short-Term Memory (BiLSTM) networks and hybrid Convolutional BiLSTM (Conv-BiLSTM) architectures, have demonstrated strong performance in text classification tasks. However, these models often struggle to capture the hierarchical and spatial relationships that are intrinsic to linguistic data. To address this limitation, this study investigates the integration of capsule networks with BiLSTM and Conv-BiLSTM architectures for mental health detection. Leveraging a real-world Reddit corpus, we conduct extensive experiments comparing baseline BiLSTM and Conv-BiLSTM models with their capsule-enhanced counterparts. Furthermore, we explore the role of advanced loss functions, such as focal loss and contrastive loss, in addressing class imbalance and mitigating boundary blurring among semantically overlapping disorders. Our findings indicate that incorporating capsule layers significantly strengthens feature representation, leading to notable improvements in accuracy and F1-score across multiple mental health categories. The study focuses on six key disorders, including depression, anxiety, borderline personality disorder (BPD), and bipolar disorder. In addition, model interpretability is enhanced using Local Interpretable Model-agnostic Explanations (LIME), which highlights the critical linguistic features driving predictions, thereby improving transparency and reliability in mental health evaluations.

由于全球对心理健康问题的关注日益增加，从社交媒体文本中检测心理健康引起了越来越多的研究关注。传统的深度学习模型，如双向长短期记忆（BiLSTM）网络和混合卷积BiLSTM （convl -BiLSTM）架构，在文本分类任务中表现出了很强的性能。然而，这些模型往往难以捕捉语言数据固有的层次和空间关系。为了解决这一限制，本研究探讨了胶囊网络与BiLSTM和convl -BiLSTM架构的整合，用于心理健康检测。利用真实世界的Reddit语料库，我们进行了广泛的实验，将基线BiLSTM和卷积BiLSTM模型与胶囊增强模型进行比较。此外，我们探讨了高级损失函数的作用，如焦点损失和对比损失，在解决类失衡和减轻语义重叠障碍中的边界模糊。我们的研究结果表明，结合胶囊层显着增强了特征表征，导致多个心理健康类别的准确性和f1得分显着提高。这项研究的重点是六种关键的疾病，包括抑郁症、焦虑症、边缘型人格障碍（BPD）和双相情感障碍。此外，使用局部可解释模型不可知论解释（LIME）增强了模型的可解释性，它突出了驱动预测的关键语言特征，从而提高了心理健康评估的透明度和可靠性。

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引用次数: 0

Enhanced Polycystic Ovary Syndrome diagnosis model leveraging a K-means based genetic algorithm and ensemble approach 基于k均值遗传算法和集成方法的多囊卵巢综合征增强诊断模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100253

Najlaa Faris , Aqeel Sahi , Mohammed Diykh , Shahab Abdulla , Siuly Siuly

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women in their childbearing years. Detecting PCOS early is crucial for preserving fertility in young women and preventing long-term health complications like hypertension, heart disease, and obesity. While costly clinical tests exist to detect PCOS, there is a growing demand for more accurate and affordable diagnostic methods. The primary objective of this research is to pinpoint the most effective PCOS features that can aid experts in early diagnosis. We introduce a feature extraction model, termed KM-GN, which combines the k-means algorithm with a genetic selection algorithm to identify the most informative features for PCOS detection. These selected features are fed into our designed model, Random Subspace-based Bootstrap Aggregating Ensembles (RSBE). To assess the performance of the proposed RSBE method, we compare it against several individual and ensemble classifiers. The effectiveness of our model is assessed using a freely accessible dataset comprising 43 traits from 541 women, of whom 177 have been diagnosed with PCOS. We employ various statistical metrics to evaluate the performance, including the confusion matrix, accuracy, recall, F1 score, precision, and specificity. The experimental outcomes demonstrate the viability of implementing our proposed model as a hardware tool for efficient detection of PCOS.

多囊卵巢综合征（PCOS）是一种影响育龄妇女的普遍激素失调。早期发现多囊卵巢综合征对于保持年轻女性的生育能力和预防高血压、心脏病和肥胖等长期健康并发症至关重要。虽然存在昂贵的临床测试来检测多囊卵巢综合征，但对更准确和负担得起的诊断方法的需求不断增长。本研究的主要目的是确定最有效的多囊卵巢综合征特征，以帮助专家进行早期诊断。我们引入了一种特征提取模型KM-GN，该模型结合了k-means算法和遗传选择算法来识别PCOS检测中最具信息量的特征。这些选择的特征被馈送到我们设计的模型，随机子空间为基础的Bootstrap聚合集成（RSBE）。为了评估所提出的RSBE方法的性能，我们将其与几个单独和集成分类器进行比较。我们的模型的有效性是使用一个免费访问的数据集来评估的，该数据集包括来自541名女性的43个特征，其中177名被诊断为多囊卵巢综合征。我们采用各种统计指标来评估性能，包括混淆矩阵、准确性、召回率、F1评分、精度和特异性。实验结果表明，将我们提出的模型作为有效检测PCOS的硬件工具是可行的。

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引用次数: 0