Clustering patch embeddings from a pre-trained breast cancer risk model identifies recurring phenotypes correlated with 5-year risk, including dense tissue, microcalcifications, and shortcut artifacts.
Alex Zwanenburg, Martin Valli`eres, Mahmoud A
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DoSReMC improves cross-domain generalization in mammography classification by fine-tuning only batch normalization and fully connected layers of pretrained CNNs while preserving convolutional filters, combined with adversarial training.
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Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
Clustering patch embeddings from a pre-trained breast cancer risk model identifies recurring phenotypes correlated with 5-year risk, including dense tissue, microcalcifications, and shortcut artifacts.
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DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
DoSReMC improves cross-domain generalization in mammography classification by fine-tuning only batch normalization and fully connected layers of pretrained CNNs while preserving convolutional filters, combined with adversarial training.