马尔可夫随机场(Markov Random Field),也有人翻译为马尔科夫随机场,马尔可夫随机场是建立在马尔可夫模型和贝叶斯理论基础之上的,它包含两层意思:一是什么是马尔可夫,二是什么是随机场。

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摘要:近年来,在开发更准确、高效的医学和自然图像分割机器学习算法方面取得了重大进展。在这篇综述文章中,我们强调了机器学习算法在医学成像领域有效和准确分割中的重要作用。我们特别关注几个关键的研究涉及到应用机器学习方法在生物医学图像分割。我们回顾了经典的机器学习算法,如马尔可夫随机场、k均值聚类、随机森林等。尽管与深度学习技术相比,这种经典的学习模型往往精度较低,但它们通常更具有样本效率,结构也更简单。我们还回顾了不同的深度学习结构,如人工神经网络(ANNs)、卷积神经网络(CNNs)和递归神经网络(RNNs),并给出了这些学习模型在过去三年中获得的分割结果。我们强调每种机器学习范式的成功和局限性。此外,我们还讨论了与不同机器学习模型训练相关的几个挑战,并提出了一些解决这些挑战的启发方法。

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Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main tractability assumption requires the treewidth of a corresponding MRF to be bounded strongly limiting the range of admissible applications. In fact, many practical problems in the area of structured prediction require modelling of global dependencies by either directly introducing global factors or enforcing global constraints on the prediction variables. That, however, always results in a fully-connected graph making exact inference by means of this algorithm intractable. Previous work [1]-[4] focusing on the problem of loss-augmented inference has demonstrated how efficient inference can be performed on models with specific global factors representing non-decomposable loss functions within the training regime of SSVMs. In this paper, we provide a more general framework for an efficient exact inference and extend the set of handleable problem instances by allowing much finer interactions between the energy of the core model and the sufficient statistics of the global terms. We demonstrate the usefulness of our method in several use cases, including one that cannot be handled by any of the previous approaches.

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