Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI

Abstract

Key points 1. Transfer learning (TL) improves performance of tumor segmentation on diffusion-weighted imaging (DWI) especially in limited case numbers. 2. Training a model by combining sufficient data of different cancers exhibited the highest performance for segmenting mixed cervical and uterine datasets and also improved the pretrained cervical model. 3. The TL model with fine-tuning the early layers of the encoder part outperformed those by fine-tuning the other layers.

Keywords

• Magnetic resonance imaging
• Deep learning
• Uterine neoplasms