Development and application of robustness evaluation techniques for ai/ml models derived from biological data
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Authors
Chuah, Joshua
Issue Date
2024-05
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Biomedical engineering
Alternative Title
Abstract
Artificial intelligence (AI) and machine learning (ML) models are frequently used to analyze large, complex biomedical datasets. These types of models are commonly used for tasks such
as disease diagnosis, biomarker identification, and network analysis. However, the data that
these models are derived from and used on are often characterized by significant amounts of
noise resulting from patient-to-patient heterogeneity, different measurement protocols, and
other commonly encountered sources of noise. This creates a problem for the robustness of
these models and one outcome is that relatively few AI/ML models have seen widespread
clinical use. As such, evaluation and subsequent improvement of AI/ML model robustness
is vital for clinical translation.
This dissertation examines methods which will allow researchers to quantify model ro
bustness, and further demonstrates how to develop more robust AI/ML models. First, this
work defines a framework which can be used to evaluate the robustness of an already-trained
biomarker-based diagnostic model. This is done by measuring the quality of the biomarkers
used to generate the classifier and observing the classifier’s performance when the data is
perturbed by several sources of noise. Next, a detailed investigation was performed that
looked at the robustness of deep learning medical image classification models in response to
being trained by data that was artificially perturbed. One key outcome from this evaluation
was that it was demonstrated that perturbing training samples results in excellent classifier
performance not only for noisy testing data but also does not sacrifice performance on unper
turbed images. This is especially important as a classifier will need to be able to perform well
on several distributions of data to truly be generalizable across multiple datasets. Finally, a
method for the creation of multi-omic co-expression networks of longitudinal biological data
was developed. The robustness of this model was assessed by noise perturbation of the data,
and further verified by comparing the model outcomes to known biological information.
By understanding how to measure and improve AI/ML model robustness, robust mod
els can be generated that perform well on diverse sets of data. In conclusion, this dissertation
lays the foundation for advancing the clinical applicability of AI/ML models by establishing
methodologies to assess and enhance their robustness in the face of inherent data noise.
Description
May2024
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY