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

• 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
• 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

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 从生物医学数据中提取的建模框架 例如电压、光谱（例如质量，磁共振， 阻抗、光吸收、.）、显微镜或放射学图像、基因 表情，还有很多。科学家们希望了解 不同分子和细胞测量之间的关系通常 面临的问题涉及破译不同细胞之间的差异， 器官测量当前的方法（例如，特征工程和 分类，端到端神经网络）通常被视为“黑匣子”， 因为它们与任何生物机械效应都没有联系。的方法 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 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，而且直接可视化， 解释。作为上一个融资期的结果，我们目前的 处理时， 在准确性方面以宽的裕度分割信号和图像， 计算复杂性，所需的训练数据量，可解释性和 对分布外样本的稳健性。在现阶段，我们力求 将该方法从分割的图像和信号推广到几乎任何 数据集类型。我们将探索在细胞计数中的概念应用的证明， 病理学和放射组学

项目成果

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

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