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How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is fundamentally driven by machine learning in general and computer vision and deep learning in particular. With the help of PAIGE.AI we are building a clinical-grade AI at Memorial Sloan Kettering Cancer Center. The models are trained based on petabytes of image and clinical data on top of the largest DGX-1 V100 cluster in pathology. The goal is not only to automated cumbersome and repetitive tasks, but to impact diagnosis and treatment decisions in the clinic. This talk will focus on our recent advances in deep learning for tumor detection and segmentation, on how we train these high capacity models with annotations collected from pathologists, and how the resulting systems are implemented in the clinic.
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