Laura Brattain

Ph.D.

University of Central Florida

Associate Professor

PLCO (Learn more about this study)

PLCOI-1619

Jul 15, 2024

Automated Lung Cancer Assessment with Explainable AI and Uncertainty Estimation

Lung cancer remains a leading cause of cancer-related mortality globally. Accurate early detection, characterization, and prognosis are crucial for improving patient outcomes. While deep learning (DL) techniques have shown promise in automating these tasks, there is a growing need for models that not only provide accurate predictions but also offer explainability and uncertainty estimation. Explainable AI (XAI) can help clinicians understand and trust model predictions, while uncertainty estimation can highlight cases that require further review. This proposal aims to develop an automated lung cancer assessment system incorporating explainable AI and uncertainty estimation.

The specific objectives are:

Automated detection of lung nodules in CT scans with uncertainty estimation.
Characterization and classification of lung nodules with explainability.
Prognostic modeling for patient survival prediction with integrated uncertainty estimation and explainability.