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

Ling Zhang
Ling Zhang
Stephanie A Harmon
Stephanie A Harmon
Jonathan Sackett
Jonathan Sackett
Deepak Kesani
Deepak Kesani
Sherif Mehralivand
Sherif Mehralivand

AJR. American journal of roentgenology, pp. 1-8, 2020.

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Abstract:

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. ...More

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