Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
| Autor: | Zuzanna Szafranowska, Richard Osuala, Bennet Breier, Kaisar Kushibar, Karim Lekadir, Oliver Diaz |
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| Rok vydání: | 2022 |
| Předmět: |
I.4.0
FOS: Computer and information sciences Computer Science - Machine Learning I.5.0 J.3 Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing I.2.0 Machine Learning (cs.LG) Artificial Intelligence (cs.AI) FOS: Electrical engineering electronic engineering information engineering |
| DOI: | 10.48550/arxiv.2203.04961 |
| Popis: | 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. Comment: Draft accepted as oral presentation at International Workshop on Breast Imaging (IWBI) 2022. 9 pages, 3 figures |
| Databáze: | OpenAIRE |
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