Transport transforms for biomedical data modeling, estimation, and classification

用于生物医学数据建模、估计和分类的传输转换

基本信息

批准号：
10672626
负责人：
Gustavo Kunde Rohde
金额：
$ 35.51万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10672626
关键词：
Address Biological Brain imaging COVID-19 patient Cells Classification Clinical Sciences Collaborations Communities Complement Complex Computer Models Computer software Cytometry Data Data Analyses Data Set Development Discipline Documentation Electronic Health Record Energy consumption Engineering Ensure Funding Future Gene Expression Genome Goals Image Imaging technology Knee Osteoarthritis Learning Letters Magnetic Resonance Magnetic Resonance Imaging Mathematics Measurement Measures Methodology Methods Modeling Molecular Optics Organ Outcome Pathology Pattern Pattern Recognition Phase Problem Solving Risk Sampling Scientist Series Signal Transduction Specific qualifier value Speed System Techniques Technology Training Visualization absorption biomedical data science breast cancer diagnosis cancer risk chemical reaction data mining data modeling data space deep learning deep learning model electric impedance experimental study high dimensionality imaging Segmentation improved innovation learning strategy lectures machine learning model mathematical model microscopic imaging morphometry multidimensional data neural network predictive modeling radiological imaging radiomics reconstruction software development technology research and development tool tumor progression virtual voltage

项目摘要

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.

该项目的目标是开发一种新的数学和计算方法从生物医学中提取的生物医学数据的建模框架实验，例如电压、光谱（例如质量、磁共振、阻抗、光吸收……）、显微镜或放射图像、基因表达，以及许多其他寻求理解的科学家。不同分子和细胞测量之间的关系通常是面临涉及破译不同细胞之间差异或当前的方法（例如特征工程和分类、端到端神经网络）通常被视为“黑匣子”，鉴于它们与任何生物机械效应缺乏联系。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。在原始数据空间中表示的任何机器学习模型，允许不仅提高了预测的准确性，而且还可以直接可视化和作为上一个资助期的结果，我们当前的解释。该方法在处理时优于其他数学建模工具在准确度方面大幅分割信号和图像，计算复杂度、所需训练数据量、可解释性和在当前阶段，我们寻求对分布样本的鲁棒性。将该方法推广到分割图像和信号之外的几乎任何领域我们将探索细胞计数中的概念验证应用，病理学和放射组学。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Neural Networks, Hypersurfaces, and the Generalized Radon Transform.

神经网络、超曲面和广义氡变换。

DOI：
10.1109/msp.2020.2978822
发表时间：
2020
期刊：
IEEE signal processing magazine
影响因子：
14.9
作者：
Kolouri,Soheil;Yin,Xuwang;Rohde,GustavoK
通讯作者：
Rohde,GustavoK

Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform.

DOI：
10.1016/j.cma.2024.116822
发表时间：
2023-08
期刊：
Computer methods in applied mechanics and engineering
影响因子：
7.2
作者：
A. Rubaiyat;D. H. Thai;J. Nichols;M. Hutchinson;S. Wallen;Christina J. Naify;Nathan Geib;M. Haberman;G. Rohde
通讯作者：
A. Rubaiyat;D. H. Thai;J. Nichols;M. Hutchinson;S. Wallen;Christina J. Naify;Nathan Geib;M. Haberman;G. Rohde

Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).

DOI：
10.3390/diagnostics13061129
发表时间：
2023-03-16
期刊：
DIAGNOSTICS
影响因子：
3.6
作者：
Miller, Matthew M.;Rubaiyat, Abu Hasnat Mohammad;Rohde, Gustavo K.
通讯作者：
Rohde, Gustavo K.

Real‐time intelligent classification of COVID‐19 and thrombosis via massive image‐based analysis of platelet aggregates

通过基于大规模图像的血小板聚集体分析对 COVID-19 和血栓形成进行实时智能分类

DOI：
10.1002/cyto.a.24721
发表时间：
2023
期刊：
Cytometry Part A
影响因子：
3.7
作者：
Zhang Chenqi;Herbig Maik;Zhou Yuqi;Nishikawa Masako;Shifat‐E‐Rabbi Mohammad;Kanno Hiroshi;Yang Ruoxi;Ibayashi Yuma;Xiao Ting‐Hui;Rohde Gustavo K.;Sato Masataka;Kodera Satoshi;Daimon Masao;Yatomi Yutaka;Goda Keisuke
通讯作者：
Goda Keisuke