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SPIE_6 Szafranowska SPIE IWBI 2022.pdf (4.41 MB)

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2022

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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/219981

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

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https://doi.org/10.1117/12.2625781

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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

Matèries

Mamografia, Càncer de mama, Aprenentatge automàtic

Matèries (anglès)

Mammography, Breast cancer, Machine learning

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Comunicacions a congressos (Matemàtiques i Informàtica)

Citació

SZAFRANOWSKA, Zuzanna, OSUALA, Richard, BREIER, Bennet, KUSHIBAR, Kaisar, LEKADIR, Karim, DÍAZ, Oliver. Sharing generative models instead of private data: a simulation study on mammography patch classification. _Comunicació a: Proc. SPIE 12286_. 16th International Workshop on Breast Imaging (IWBI2022). Vol.  122860Q (13 July 2022). [consulta: 21 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/219981]

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