Development of Novel Attention-Aware Deep Learning Models and Their Applications in Computer Vision and Dynamical System Calibration

In recent years, deep learning has revolutionized computer vision and natural language processing tasks, but the black-box nature of these models poses significant challenges for their interpretability and reliability, especially in critical applications such as healthcare. To address this, attention-based methods have been proposed to enhance the focus and interpretability of deep learning models. In this dissertation, we investigate the effectiveness of attention mechanisms in improving prediction and modeling tasks across different domains. We propose three essays that utilize task-specific designed trainable attention modules in manufacturing, healthcare, and system identification applications. In essay 1, we introduce a novel computer vision tool that tracks the melt pool in X-ray images of laser powder bed fusion using attention modules. In essay 2, we present a mask-guided attention (MGA) classifier for COVID-19 classification on lung CT scan images. The MGA classifier incorporates lesion masks to improve both the accuracy and interpretability of the model, outperforming state-of-the-art models with limited training data. Finally, in essay 3, we propose a Transformer-based model, utilizing self-attention mechanisms, for parameter estimation in system dynamics models that outpaces the conventional system calibration methods. Overall, our results demonstrate the effectiveness of attention-based methods in improving deep learning model performance and reliability in diverse applications.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Maftouni, Maede

Chair dc:contributor.committeechair

• Kong, Zhenyu

Committee members dc:contributor.committeemember

• Chen, Xi
• Ghaffarzadegan, Navid
• Yue, Xiaowei

• Deep Learning
• Attention Mechanism
• Transformer
• Computer Vision
• Video Object Segmentation
• Manufacturing
• Healthcare
• System Dynamics

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37747

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115755