Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification. (arXiv:2203.04961v1 [eess.IV])

Early detection of breast cancer in mammography screening via deep-learning
based computer-aided detection systems shows promising potential in improving
the curability and mortality rates of breast cancer. However, many clinical
centres are restricted in the amount and heterogeneity of available data to
train such models to (i) achieve promising performance and to (ii) generalise
well across acquisition protocols and domains. As sharing data between centres
is restricted due to patient privacy concerns, we propose a potential solution:
sharing trained generative models between centres as substitute for real
patient data. In this work, we use three well known mammography datasets to
simulate three different centres, where one centre receives the trained
generator of Generative Adversarial Networks (GANs) from the two remaining
centres in order to augment the size and heterogeneity of its training dataset.
We evaluate the utility of this approach on mammography patch classification on
the test set of the GAN-receiving centre using two different classification
models, (a) a convolutional neural network and (b) a transformer neural
network. Our experiments demonstrate that shared GANs notably increase the
performance of both transformer and convolutional classification models and
highlight this approach as a viable alternative to inter-centre data sharing.

Source: https://arxiv.org/abs/2203.04961

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