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Found 4 papers, 1 papers with code
Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data.
Examining the Capability of GANs to Replace Real Biomedical Images in Classification Models Training
The possibility of the use of artificial images instead of real ones for training machine learning models was examined by benchmark classification tasks being solved using conventional and deep learning methods.
Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks
We concluded that: (1) An increase of the amplitude of perturbation in generating malicious adversarial images leads to a growth of the fraction of successful attacks for the majority of image types examined in this study.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
