Zavar Matineh, Ghaffari Hamid Reza, Tabatabaee Hamid
Department, of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
Department, of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Sci Rep. 2025 Aug 13;15(1):29666. doi: 10.1038/s41598-025-99783-2.
Accurate detection and segmentation of lesions induced by High-Intensity Focused Ultrasound (HIFU) in medical imaging remain significant challenges in automated disease diagnosis. Traditional methods heavily rely on labeled data, which is often scarce, expensive, and time-consuming to obtain. Moreover, existing approaches frequently struggle with variations in medical data and the limited availability of annotated datasets, leading to suboptimal performance. To address these challenges, this paper introduces an innovative framework called the Optimized Multi-Task Contrastive Learning Framework (OMCLF), which leverages self-supervised learning (SSL) and genetic algorithms (GA) to enhance HIFU lesion detection and segmentation. OMCLF integrates classification and segmentation into a unified model, utilizing a shared backbone to extract common features. The framework systematically optimizes feature representations, hyperparameters, and data augmentation strategies tailored for medical imaging, ensuring that critical information, such as lesion details, is preserved. By employing a genetic algorithm, OMCLF explores and optimizes augmentation techniques suitable for medical data, avoiding distortions that could compromise diagnostic accuracy. Experimental results demonstrate that OMCLF outperforms single-task methods in both classification and segmentation tasks while significantly reducing dependency on labeled data. Specifically, OMCLF achieves an accuracy of 93.3% in lesion detection and a Dice score of 92.5% in segmentation, surpassing state-of-the-art methods such as SimCLR and MoCo. The proposed approach achieves superior accuracy in identifying and delineating HIFU-induced lesions, marking a substantial advancement in medical image interpretation and automated diagnosis. OMCLF represents a significant step forward in the evolutionary optimization of self-supervised learning, with potential applications across various medical imaging domains.
在医学成像中，准确检测和分割高强度聚焦超声（HIFU）诱导的病变仍然是自动疾病诊断中的重大挑战。传统方法严重依赖标记数据，而获取这些数据往往稀缺、昂贵且耗时。此外，现有方法常常难以应对医学数据的变化以及注释数据集的有限可用性，导致性能欠佳。为应对这些挑战，本文引入了一种名为优化多任务对比学习框架（OMCLF）的创新框架，该框架利用自监督学习（SSL）和遗传算法（GA）来增强HIFU病变的检测和分割。OMCLF将分类和分割集成到一个统一模型中，利用共享骨干提取共同特征。该框架系统地优化了针对医学成像量身定制的特征表示、超参数和数据增强策略，确保保留诸如病变细节等关键信息。通过采用遗传算法，OMCLF探索并优化适合医学数据的增强技术，避免可能损害诊断准确性的失真。实验结果表明，OMCLF在分类和分割任务中均优于单任务方法，同时显著降低了对标记数据的依赖。具体而言，OMCLF在病变检测中达到了93.3%的准确率，在分割中达到了92.5%的Dice分数，超过了SimCLR和MoCo等现有最先进的方法。所提出的方法在识别和描绘HIFU诱导的病变方面实现了卓越的准确性，标志着医学图像解释和自动诊断取得了重大进展。OMCLF代表了自监督学习进化优化中的重要一步，在各个医学成像领域具有潜在应用。
