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Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers
Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images.
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
Energy is an essential, but often forgotten aspect in large-scale federated systems.
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks
Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space.
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
This significant speedup enables a wide range of practical applications based on privacy-preserved deep neural networks.
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