提出DEA方法，从高维生物医学数据中自动发现关键差异特征。
Discovering distinctive elements of biomedical datasets for high-performance exploration
- 基于深度学习的无监督方法，利用数据相关性提取差异元素。
- 在疾病检测、基因排序等任务中准确率最高提升45%。
- 支持用户干预中间过程，结果更可解释，适合临床研究者使用。
人脑通过少量基本元素表征物体，并根据元素差异区分不同物体。因此，在众多以感知为导向的生物医学与临床研究中，发现高维数据集中的差异特征至关重要。然而，目前尚无可靠方法可用于高维生物医学和临床数据集中差异元素的提取。本文提出一种名为差异元素分析（DEA）的无监督深度学习技术，利用数据集的高维相关性信息提取差异数据元素。DEA首先生成大量数据差异片段，再通过独特的核驱动三重优化网络对这些片段进行过滤与压缩，形成DEA元素。实验表明，DEA在医学图像疾病检测、基因排序以及单细胞RNA测序（scRNA-seq）数据中的细胞识别等应用中，相比传统方法准确率最高提升45%。此外，DEA支持用户对中间计算过程进行引导式操作，从而获得更具可解释性的中间结果。
原文摘要 · Abstract (English)
The human brain represents an object by small elements and distinguishes two objects based on the difference in elements. Discovering the distinctive elements of high-dimensional datasets is therefore critical in numerous perception-driven biomedical and clinical studies. However, currently there is no available method for reliable extraction of distinctive elements of high-dimensional biomedical and clinical datasets. Here we present an unsupervised deep learning technique namely distinctive element analysis (DEA), which extracts the distinctive data elements using high-dimensional correlative information of the datasets. DEA at first computes a large number of distinctive parts of the data, then filters and condenses the parts into DEA elements by employing a unique kernel-driven triple-optimization network. DEA has been found to improve the accuracy by up to 45% in comparison to the traditional techniques in applications such as disease detection from medical images, gene ranking and cell recognition from single cell RNA sequence (scRNA-seq) datasets. Moreover, DEA allows user-guided manipulation of the intermediate calculation process and thus offers intermediate results with better interpretability.
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