Accordingly, we visualize the features extracted from different abnormal tissue types of the segmented tumor. The visualization is performed using one of the most widely used high-dimensional data visualization techniques known as t-Distributed Stochastic Neighbor Embedding 中文解释

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基于python+OpenCV的全景图像拼接系统源码数据库论文.docx

t-Distributed Stochastic Neighbor Embedding（t-SNE）是一种强大的高维数据可视化技术，用于将高维数据可视化为2D或3D空间。它是一种非线性降维技术，特别擅长保留数据的局部结构。 在t-SNE中，算法首先计算高维数据点之间的相似度概率，其中相似度基于数据点之间的欧几里得距离。然后，它尝试将高维数据点表示为低维空间（例如2D或3D），使得数据点之间的相似度仍尽可能保持不变。这是通过最小化高维概率分布和数据点的低维概率分布之间的差异来实现的。 在2D或3D空间中的数据点的可视化可以揭示类似数据点的聚类或数据中的模式，这在高维空间中可能难以辨别。在医学图像领域，t-SNE可用于可视化分割肿瘤的不同异常组织类型中提取的特征，正如问题中所述。
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