publications

2022

1. SPIE

   DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging

   Anees Kazi, Viktoria Markova, Prabhat R. Kondamadugula, and 4 more authors

   In Medical Imaging 2022: Computer-Aided Diagnosis 2022

   Abs Bib

   Deep learning has revolutionized neuroimaging studies of psychiatric and neurological disorders. The difference between brain age and chronological age is a useful biomarker for identifying neurological disorders. Furthermore, delineating both age and gender is important for the study of illnesses exhibiting the phenotypic difference in these. In this paper, we focus on the prediction of age and gender from brain connectomes data which is a step further to full automation of disease prediction. We model the connectomes as brain graphs. Data is collected as functional MRI (fMRI) signals and the graphs are created by binarizing the correlation among the fMRI signals at the brain parcels considered as nodes. Such a graph represents the neurobiological functional connectivity. We further differentiate between static and dynamic connectivity. The former is constructed with the correlation of the overall signal at the nodes, while the latter is modeled as a sequence of brain graphs constructed over sequential time periods. Our hypothesis is that leveraging information from both the static and dynamic functional connectivity is beneficial to the task at hand. Our main contribution lies in our proposed novel input data representation and proposed recurrent graph-based deep learning model setting together. The proposed Dynamic Graph-based Gated Recurrent Unit (DG-GRU) comprises a mechanism to process both types of connectivities. In addition, it can be easily incorporated into any deep neural model. We show a thorough analysis of the model on two publicly available datasets HCP and ABIDE for two tasks to show the superiority of the model.

   @inproceedings{10.1117/12.2607469,
     author = {Kazi, Anees and Markova, Viktoria and Kondamadugula, Prabhat R. and Liu, Beiyan and Adly, Ahmed and Faghihroohi, Shahrooz and Navab, Nassir},
     title = {{DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging}},
     volume = {12033},
     booktitle = {Medical Imaging 2022: Computer-Aided Diagnosis},
     editor = {Drukker, Karen and Iftekharuddin, Khan M. and Lu, Hongbing and Mazurowski, Maciej A. and Muramatsu, Chisako and Samala, Ravi K.},
     organization = {International Society for Optics and Photonics},
     publisher = {SPIE},
     pages = {1203312},
     keywords = {Graph Convolutions, Deep Learning, GRU},
     year = {2022},
     doi = {10.1117/12.2607469},
     url = {https://doi.org/10.1117/12.2607469}
   }

2021

1. AAAI-MLPS

   Generalized Physics-Informed Machine Learning for Numerically Solved Transient Physical Systems

   Leela Sai Prabhat Reddy Kondamadugula, and Rishith Ellath Meethal

   2021

   Abs Bib PDF

   We introduce a generalized physics-informed machine learning workflow to accurately predict the behavior of a transient physical system with enhanced physics conformity. A physics-guided machine learning (PGML) model is developed to achieve this goal. Our model consists of two main parts for a given transient system: (1) a physics-based numerical model which solves the system using conventional numerical methods and returns the stiffness matrix and force vector at each time step; (2) a neural network (NN) based machine learning (ML) surrogate model which predicts the solution of the system using a custom physics-guided loss function constructed from system matrix and force vector. The proposed workflow results in a physics-aware Machine Learning (ML) model. Such a trained model can be used to avoid the prohibitively expensive step of running a transient system simulation at the desired resolutions in space and time. We demonstrate and test the model on single-degree-of-freedom (SDOF) and multiple-degree-of-freedom (MDOF) system’s examples from structural dynamics. Our results show that the method predicts the simulation results accurately. The proposed workflow can be directly adapted to any other physics and numerical method as it is not tailored towards a specific physics or a numerical method.

   @article{meethal2021generalized,
     title = {Generalized Physics-Informed Machine Learning for Numerically Solved Transient Physical Systems},
     author = {Kondamadugula, Leela Sai Prabhat Reddy and Meethal, Rishith Ellath},
     url = {https://youtu.be/BcCfCtUsNMc},
     year = {2021}
   }