# name
Sharing generative models instead of private data: a simulation study on mammography patch classification

# metadata.dc.description.abstract[].value
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. Find our code at https://github.com/
zuzaanto/mammo_gans_iwbi2022

# metadata.dc.relation.ispartof[].value
Comunicació a: Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860Q (13 July 2022)

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Sharing generative models instead of private data: a simulation study on mammography patch classification