Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications

In recent decades, deep learning (DL) has emerged as a powerful tool for analyzing complex patterns in large-scale healthcare data, significantly advancing diagnosis, prognosis, and treatment planning. However, the collection of medical data faces inherent limitations, including invasiveness, high cost, expert labeling requirements, disease rarity, and patient recruitment challenges. This thesis addresses these constraints by developing novel knowledge-informed, image-based DL methodologies that enhance sample efficiency, predictive accuracy, and generalizability for real-world cancer applications. The proposed models systematically integrate biological, anatomical, and clinical domain knowledge into DL pipelines to overcome data scarcity and heterogeneity in cancer imaging. They combine self-supervised pretraining, knowledge-informed loss functions, hierarchical and contextualized architectures, and label smoothing techniques that distill clinical and biological priors. These approaches enable dense spatial prediction of gene modules, genetic alterations, and segmentation of heterogeneous tumors with vague boundaries, even under sparse supervision. Across applications in glioblastoma and liver cancer, the methods demonstrate substantial improvements in generalizability and precision, showing strong potential to support personalized diagnosis, prognosis, treatment planning, and monitoring in precision oncology.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Wang, Hairong

Advisor dc:contributor.advisor

• Li, Jing

Committee members dc:contributor.committeemember

• Shi, Jianjun
• Xian, Xiaochen
• Swanson, Kristin
• Huang, Shuai

• Deep learning
• Knowledge-informed models
• Weakly supervised learning

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/78670

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/78670