标题： X射线可转移的多表示学习 显示英文标题

标题： X-ray transferable polyrepresentation learning

摘要： 机器学习算法的成功本质上与有意义特征的提取有关，因为它们在这些算法的性能中起着关键作用。 这一挑战的核心是数据表示的质量。 然而，从未见过的数据集中有效泛化和提取这些特征的能力也同样至关重要。 鉴于此，我们引入了一个新概念：多表示法。 多表示法整合了从不同来源提取的同一模态的多种表示，例如来自孪生网络的向量嵌入、自监督模型和可解释的放射组学特征。 与依赖单一表示相比，这种方法能产生更好的性能指标。 此外，在X射线图像的背景下，我们展示了创建的多表示法在较小数据集上的可迁移性，强调了其作为各种图像相关解决方案中实用且资源高效方法的潜力。 值得注意的是，以医疗数据为例的多表示法概念也可以应用于其他领域，展示了其灵活性和广泛的应用潜力。 显示英文摘要

摘要： The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Central to this challenge is the quality of data representation. However, the ability to generalize and extract these features effectively from unseen datasets is also crucial. In light of this, we introduce a novel concept: the polyrepresentation. Polyrepresentation integrates multiple representations of the same modality extracted from distinct sources, for example, vector embeddings from the Siamese Network, self-supervised models, and interpretable radiomic features. This approach yields better performance metrics compared to relying on a single representation. Additionally, in the context of X-ray images, we demonstrate the transferability of the created polyrepresentation to a smaller dataset, underscoring its potential as a pragmatic and resource-efficient approach in various image-related solutions. It is worth noting that the concept of polyprepresentation on the example of medical data can also be applied to other domains, showcasing its versatility and broad potential impact.