Theory and application of neural and graphical models in early cancer diagnostics
Author(s)
Radhakrishnan, Adityanarayanan
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Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Caroline Uhler.
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With the recent availability of large training datasets and graphics processing units (GPUs), we address challenges in the application of graphical models and neural networks to prediction sensitive areas such as healthcare. We begin by presenting our work in the context of learning graphical models from biological data. Namely, we present a combinatorial perspective of Markov Equivalence Classes (MECs), which defines the size of solution spaces when attempting to learn a graphical model from data. Through our analysis, we show that the size of these MECs can be exponential with respect to features of the graph (such as average degree). We then switch contexts to address the challenge of developing interpretable complex models. Namely, we present a variational-inference-motivated neural network, PatchNet, that provides visual interpretability, and we present the application of our network to the Describable Textures Dataset (DTD), the ISIC-ISBI Melanoma Classification Challenge, and cell nucleus data.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. "May 2017." Hand written on title page: "June 2017." Includes bibliographical references (pages 69-72).
Date issued
2017Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.