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Abstract #0809

Transfer Learning-Based Preoperative Prediction of Lymph Node Metastasis

Renee Cattell1, Jie Ding1, Shenglan Chen1, and Chuan Huang1,2,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Radiology, Stony Brook University, Stony Brook, NY, United States, 3Psychiatry, Stony Brook University, Stony Brook, NY, United States

A tool to preoperatively predict sentinel lymph node status in patients with breast cancer could minimize the need for invasive surgical examination. Radiomics has been shown to have predictive power in many classification tasks. Fully automated deep learning methods would integrate more easily into clinical workflow because they do not require manual feature extraction. However, convolutional neural networks are computationally demanding and require large datasets to train. Transfer learning can be applied to allow for shortened training time and applicable to relatively small datasets.

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