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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