(Georgia Institute of Technology, 2023-12-13)
Saha, Debbrata Kumar
In the neuroimaging domain, the data collection process is expensive, and attempting to pool data from multiple imaging sites faces numerous challenges, including variations in data acquisition protocols from site to site. There is also concern associated with revealing the identities of rare disease subjects. The challenges of data sharing associated with these issues prevent the datasets from being as large as desired, ultimately hindering the benefits of utilizing large-scale datasets in research operations. This dissertation aims to address these potential challenges. First, we develop a federated embedding algorithm to assess the quality control of neuroimaging datasets. Our algorithm has demonstrated superior performance in overcoming challenges that some notable existing algorithms struggle to solve. Subsequently, we introduce a privacy-preserving algorithm tailored to the neuroimaging domain, ensuring formal mathematical privacy guarantees during message passing in federated computation. The integration of this algorithm with the existing software platform for federated neuroimaging has been demonstrated, making our methods readily available as tools for neuroimaging users worldwide.
Our third proposed approach emphasizes fast federated communication with more stringent privacy assurances. Lastly, we design a federated algorithm to extract multivariate patterns (covarying networks) from structural magnetic resonance imaging (sMRI) data for the analysis of brain morphometry. These four proposed methods enable neuroimaging users to perform operations in a federated environment where it is not possible to run operations centrally in typical scenarios.
(Georgia Institute of Technology, 2023-12-11)
Lewis, Noah Jerome
Neuroimaging data, which can be highly complex and occasionally inscrutable, requires robust, reproducible, and domain-specific methods. Deep learning and model explainability have become common methods for analyzing neuroimaging data. However, the complex, obscure, and sometimes flawed nature of both deep learning and explainability compound the difficulties in neuroimaging analysis. This dissertation addresses several of these issues with explainability by employing a framework of constraint-based solutions. These constraints span the entire modeling pipeline, including initialization, model parameters and gradients, and the loss functions. To familiarize the readers with the field, this dissertation will begin with a comprehensive investigation into current explainability methods both in general and specific to neuroimaging, then describe the three constraint-based methodologies that comprise this framework. First, we develop an attention-based constraint for recurrent models that resolves vanishing saliency. Vanishing saliency is closely related to vanishing gradients, a common issue for training, in which the gradients lose value during backpropagation. Our second proposed method is a set of initialization constraints that target underspecification and its implications for post-hoc explanations. Our final proposed method leverages inherent neuroimaging-based geometric information in the input to constrain the optimization approach to produce more interpretable models. These three constraint methods amount to a broad framework that provides a robust and reproducible explanatory system appropriate for neuroimaging.
(Georgia Institute of Technology, 2023-10-03)
Baker, Bradley Thomas
The enclosed research is a focused empirical and theoretical analysis of the
optimization methods in machine learning, and the underlying role that the
matrix rank of utilized learning statistics plays in these algorithms. We show
that this new perspective on machine learning optimization provides benefits in terms of communication-efficient federated learning algorithms, as well as novel insights in terms of model introspection and theory of learning dynamics. In applications to the complex domain of Neuroimaging data analysis, we show
that this rank-focused frame of reference allows for unique insights into how
models perform on particular populations.