Memory-aware curriculum federated learning for breast cancer classification
Computer Methods and Programs in Biomedicine(2021)
Abstract
•Curriculum learning improves breast cancer classification on high-resolution mammograms in a federated setting.•Curriculum is implemented as a data scheduler, which penalizes inconsistent predictions, to improve the consistency of local models in a federated setting.•We track the predictions before and after the deployment of the global model, thus we refer to our method as memory-aware curriculum federated learning.•Memory-aware curriculum federated learning improves the classification and alignment between domain pairs.•Effective for the multi-site breast cancer classification on clinical datasets from different vendors.
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Key words
Curriculum learning,Data scheduling,Data sharing,Domain adaptation,Federated learning,Malignancy classification,Mammography
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