Deep learning is an important tool that can help drive important new innovations in medicine, including in MRI tumor segmentation for HCC. Large annotated data sets will be needed for effective deep learning, however, current techniques are tedious and inefficient for annotating images on a large scale. We propose a streamlined infrastructure to optimize and standardize the process of anonymizing patient information, structuring the data, and annotating images efficiently. We show that our streamlined infrastructure increases the speed at which ground truth annotations can be generated.
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