Transport transforms for biomedical data modeling, estimation, and classification

该项目的目标是开发一种新的数学和计算方法 用于从生物医学提取的生物医学数据的建模框架 诸如电压、光谱(例如，质量、磁共振、 阻抗、光吸收、…)、显微镜或放射图像、基因 表情，还有许多其他的。科学家们希望了解 不同的分子和细胞测量之间的关系通常是 面对涉及破译不同细胞之间的差异或 器官测量。当前的方法(例如，功能工程和 分类、端到端的神经网络)通常被视为“黑匣子”， 考虑到它们与任何生物机制效应之间缺乏联系。该方法 我们建议从头开始构建一个全新的建模框架 基于最近开发的可逆变换构建。因此，它允许 任何要在原始数据空间中表示的机器学习模型，允许 不仅提高了预测的准确性，而且还直接可视化和 释义。作为上一个资助期的结果，我们目前的 在处理过程中，方法的性能优于其他数学建模工具 在精确度方面大幅分割信号和图像， 计算复杂性、所需的训练数据量、可解释性和 对非分布样本的稳健性。在当前阶段，我们寻求 将该方法推广到分割图像和信号之外的几乎任何 数据集类型。我们将探索概念验证在细胞学中的应用， 病理学和放射组学。

The goal of the project is to develop a new mathematical and computational modeling framework for from biomedical data extracted from biomedical experiments such as voltages, spectra (e.g. mass, magnetic resonance, impedance, optical absorption, …), microscopy or radiology images, gene expression, and many others. Scientists who are looking to understand relationships between different molecular and cellular measurements are often faced with questions involving deciphering differences between different cell or organ measurements. Current approaches (e.g. feature engineering and classification, end-to-end neural networks) are often viewed as “black boxes,” given their lack of connection to any biological mechanistic effects. The approach we propose builds from the “ground up” an entirely new modeling framework build based on recently developed invertible transformation. As such, it allows for any machine learning model to be represented in original data space, allowing for not only increased accuracy in prediction, but also direct visualization and interpretation. As an outcome of the previous funding period, our current approach outperforms other mathematical modeling tools when processing segmented signals and images by a wide margin in terms of accuracy, computational complexity, amount of training data needed, interpretability and robustness to out of distribution samples. In this current phase we seek to generalize the method beyond segmented images and signals to virtually any dataset type. We will explore proof of concept applications in cytometry, pathology, and radiomics.