Wykaz obszarów badawczych związanych z tagiem Synthetic-data:
| # | Obszar badawczy | Dziedzina naukowa |
|---|---|---|
| 1 |
Synthetic Data Generation for Machine Learning-Based Computer-Aided Diagnosis in Spinal Computed Tomography Machine learning-based computer-aided diagnosis in computed tomography (CT) is often limited by the scarcity of large, well-annotated clinical datasets. Acquiring representative CT examinations of vertebral fractures with expert annotations is particularly challenging because many clinically important fracture types occur infrequently, leading to severe class imbalance and reduced model generalization. This doctoral research aims to develop and evaluate methodologies for generating realistic synthetic three-dimensional CT data to support automated vertebral fracture classification. The work will focus on supervised deep learning models implementing the nine-class AO Spine fracture classification system. The research will investigate multiple approaches to synthetic volumetric CT generation, including generative artificial intelligence models and anatomically constrained non-rigid transformations derived from clinical CT examinations. A reference dataset of expert-annotated clinical CT scans will be used for model development, parameter optimization, and independent validation. A central objective is to quantify the effect of different synthetic data generation strategies on the performance, robustness, calibration, and generalization of deep learning models. Classifiers trained on real, synthetic, and hybrid datasets will be systematically compared using independent clinical CT examinations excluded from the training process. Particular attention will be given to improving the recognition of underrepresented fracture classes while maintaining high diagnostic performance across all AO Spine categories. The expected scientific contribution is the development of methodologies for generating anatomically realistic synthetic CT data and a comprehensive evaluation of their effectiveness in mitigating data scarcity in machine learning for computer-aided diagnosis.The research is expected to establish methodological principles for integrating synthetic data into medical imaging workflows and to improve the reliability and generalizability of automated vertebral fracture classification.
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