EsViT self-supervised pretraining of a Swin Transformer plus a CNN patch branch yields AUC 0.864 on CMMD and 0.889 on INbreast for benign versus malignant mammogram classification.
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Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network
EsViT self-supervised pretraining of a Swin Transformer plus a CNN patch branch yields AUC 0.864 on CMMD and 0.889 on INbreast for benign versus malignant mammogram classification.