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Abstract #3194

Toward Universal Tumor Segmentation on Diffusion-Weighted MRI: Transfer Learning from Cervical Cancer to All Uterine Malignancies

Yu-Chun Lin1, Yenpo Lin1, Yen-Ling Huang1, Chih-Yi Ho1, and Gigin Lin1,2
1Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 2Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan


This study retrospectively analyzed diffusion-weighted MRI in 320 patients with malignant uterine tumors (UT). A pretrained model was established for cervical cancer dataset. Transfer learning (TL) experiments were performed by adjusting fine-tuning layers and proportions of training data sizes. When using up to 50% of the training data, the TL models outperformed all the models. When the full dataset was used, the aggregated model exhibited the best performance, while the UT-only model exhibited the best in the UT dataset. TL of tumor segmentation on diffusion-weighted MRI for all uterine malignancy is feasible with limited case number.

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