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Abstract #0418

An RNN and Autoencoder-based Deep Learning Approach for Detecting Brain Metastases in MRI

Shuyang Zhang1, Min Zhang2, Xinhua Cao3, Geoffrey S Young2, and Xiaoyin Xu2
1University of Michigan-Ann Arbor, Ann Arbor, MI, United States, 2Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States, 3Boston Children’s Hospital, Boston, MA, United States

Cancer metastases to the brain is a major cause of fatality in patients. Finding all the metastases is crucial to clinical treatment planning as today’s radiation therapy can target up to 20 individual metastases, making it necessary for clinicians to detect and marking multiple metastases in practice. Detecting brain metastases, however, is very challenging because the objects are small and of low contrast. Computer-aided detection of metastases can be highly valuable to improve the accuracy and efficiency of a human reader. In this work, we developed a deep learning-based pipeline for finding metastases on brain MRI.

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