Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model

摘要

OBJECTIVE. Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation.