Advanced nnU-Net Ensemble Techniques for Lung Nodule Segmentation

This thesis outlines a comprehensive research program aimed at advancing medical
image segmentation through enhanced nnU-Net ensemble methodologies. Building upon
substantial experimental results that demonstrate significant improvement over existing
approaches, the proposed research addresses critical gaps in current medical imaging AI
capabilities.  The research aims to establish new performance standards for automated lung
nodule detection with current achievements of 0.84 dice coefficient representing a 29.2%
improvement over SAM baseline and approaching clinical utility thresholds. Future work will
focus on Vision Transformer integration with nnU-Net architectures, generalization validation
across additional lung imaging datasets, and clinical deployment optimization. The expected
outcomes include significant academic contributions through peer-reviewed publications,
practical clinical applications with potential for real-world healthcare impact, and establishment
of open-source implementations for research community adoption.