PhD from the University of Rouen Normandie
Deep Learning for Brain Imaging:
- Validation of Deep Learning Segmentation Models
- Segmentation and Survival analysis of Lymphoma in Brain MRI
Reporting standard errors and confidence intervals is crucial in medical image segmentation research, as accurately measuring the level of improvement achieved is challenging. Current methods often report empirical standard deviation, which is biased due to data inter-dependency between folds. In this project, we focus on estimating confidence intervals in order to determine the scientific contribution made by improved performance over the baseline using experiments on 3D image segmentation, providing a more accurate and reliable measure of performance.