Multi-view Framework for Histomorphologic Classification

Current routine histopathologic evaluation of prostate cancer does not fully account for some individual morphology patterns associated with poor outcome. Pathologists evaluate and score morphology across multiple magnifications, motivating deep learning methods to incorporate various resolutions. We have evaluated a proof-of-concept multi-view framework to classify high risk morphology architectures that does not rely on ensemble-based techniques of multi-magnification models.

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