随着深度学习技术的迅速发展,关键点检测技术在医学影像分析中的应用受到广泛关注,尤其在超声、CT和MRI等医学影像中表现出巨大的潜力。文章首先回顾了传统的关键点检测技术与基于深度学习的关键点检测技术在医学影像中的应用,重点分析了卷积神经网络(CNN)、Hourglass网络和Transformer模型的特点与优势;随后讨论了关键点检测在医学影像中的实际应用,包括人体姿势估计、器官与肿瘤的分割与定位等领域的应用。此外,文章还总结了当前技术面临的挑战,如数据不足、图像噪声、跨设备泛化等问题,并提出了可能的解决方案。最后,结合深度学习的最新进展,本文展望了医学影像中关键点检测技术的未来发展趋势,旨在为医学影像分析中的关键点检测技术的研究与应用提供理论支持和发展思路。With the rapid development of deep learning technology, the application of keypoint detection technology in medical image analysis has received widespread attention, especially in medical images such as ultrasound, CT, and MRI, showing great potential. The article first reviews the application of traditional keypoint detection techniques and deep learning based keypoint detection techniques in medical imaging, with a focus on analyzing the characteristics and advantages of convolutional neural networks (CNN), Hourglass networks, and Transformer models;Subsequently, the practical applications of keypoint detection in medical imaging were discussed, including human pose estimation, segmentation and localization of organs and tumors, and other fields. In addition, the article also summarizes the challenges currently faced by technology, such as severe data shortages, image noise, cross device generalization, and proposes possible solutions. Finally, based on the latest advances in deep learning, this article looks forward to the future development trends of keypoint detection technology in medical imaging, aiming to provide theoretical support and development ideas for the research and application of keypoint detection technology in medical image analysis.
太赫兹(THz)技术是一个不断发展的科学领域,其特征是频谱为0.1到10 THz。太赫兹(THz)波具有独特的物理特性,可产生多种生物学效应,如刺激细胞增殖、触发细胞凋亡、提高DNA甲基化水平、增强细胞膜通透性、增强基因表达、增强细胞炎症反应、影响神经元调控等。本文综述了THz波与生物分子的相互作用,重点阐述了THz辐射对肿瘤细胞生长抑制、诱导凋亡、生物分子甲基化等方面的影响。着重对电磁生物学的生物学效应进行研究,为电磁生物学的研究提供帮助和启示。Terahertz (THz) technology is a growing scientific field characterized by a spectrum of 0.1 to 10 THz. Terahertz (THz) waves have unique physical properties and can produce a variety of biological effects, such as stimulating cell proliferation, triggering apoptosis, increasing DNA methylation level, enhancing cell membrane permeability, enhancing gene expression, and enhancing cellular inflammatory response. This paper reviews the interaction between THz waves and biomolecules, focusing on the effects of THz radiation on tumor cell growth inhibition, apoptosis induction, and methylation of biomolecules. This paper focuses on the biological effects of electromagnetic biology to provide help and enlightenment for the research of electromagnetic biology.
实例分割是图像分割的重要组成部分,同时也是计算机视觉中的一个关键研究课题,广泛应用于自动驾驶和安全监控等领域。然而,由于道路场景通常具有复杂性、多样性和杂乱的特点,处理这些场景变得尤为挑战。针对道路场景图像实例分割难度大、精度低、定位不精确等问题,本文提出一种基于改进YOLOv5(You Only Look Once version 5)的道路场景实例分割算法。以YOLOv5为基础模型,在Head模块中采用RFAConv(Receptive-Field Attention Convolution)卷积代替部分传统卷积,它全面解决了卷积核的参数共享问题,考虑到接受域中每个特征的重要性,提供了几乎可以忽略不计的计算成本和参数增量,能够更好地捕捉和融合图像特征,提升分割的精度和鲁棒性。采用ShapeIOU代替YOLOv5中原损失函数CIOU(Complete-IoU),通过聚焦边框自身形状与自身尺度计算损失,使得边框回归更为精确,能够有效提升检测效果且优于现有方法。实验结果表明:与原模型相比,改进后的模型的分割精度mAP50(mean Average Precision)达到了33.8%,相较于YOLOv5s,优化后的模型在分割精度上提高了1.2%,能够更加高效地完成道路场景的图像分割任务。
文章深入研究了在相互依存网络中引入可控耦合机制以及融合节点适应度特性对意见动态特性的影响。通过概率互联和通信控制耦合强度,实现了对网络间交互程度的精准调节。系统中的每个代理不仅具有二元意见状态(+或−),还包含适应度参数k,该参数随时间演化,其分布与代理的意见状态耦合,使得系统动态地发展出异质性和类似记忆的行为特征。通过调整概率h,即控制高适应度节点采取特定意见的倾向性,文章研究了在不同h条件下,系统收敛时间τ与系统规模N之间的关系。结果表明,τ~N,即二者呈线性关系。此外,耦合强度的变化会引发收敛时间的突变,反映出系统对该参数的高度敏感性。强耦合会加剧k值分布的极化性,导致分布呈现明显的群集态和辐射态。这种现象增强了相互依存网络间的同步性和一致性。然而,当初始意见分布满足d > 0.5(即+状态占比大于50%)时,耦合强度的增加会加剧网络间的纠缠程度,从而显著减缓系统的收敛过程。这些研究结果揭示了耦合机制与节点适应度在塑造复杂相互依存网络意见形成动态过程中的关键作用。This paper conducted an in-depth study of opinion dynamics on interdependent networks by introducing a controllable coupling mechanism and integrating node fitness characteristics into the system. The coupling strength is regulated through probabilistic interconnections and communication channels, enabling precise control over the degree of interaction between networks. Each agent in the system is characterized not only by a binary opinion state (+ or −) but also by a fitness parameter k, which evolves over time. The distribution of k is dynamically coupled with the opinions of the agents, allowing the system to naturally develop heterogeneity and memory-like behavior. By adjusting the probability h, which governs the tendency of high-fitness nodes to adopt specific opinions, this paper examined the relationship between the system’s convergence time (τ) and its size (N) under varying coupling strengths. Our findings reveal that τ~N, indicating a linear relationship. Moreover, changes in coupling strength can induce abrupt transitions in convergence time, reflecting a highly sensitive dependence on this parameter. Strong coupling amplifies the polarization of the k-value distribution, leading to the emergence of distinct clustered and radial states. This phenomenon enhances synchronization and consensus across interdependent networks. However, when the initial distribution of + and − states satisfies d > 0.5, an increase in coupling strength exacerbates the entanglement between networks, thereby significantly slowing down the converge
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