… either require steps of manual operations or achieve only moderate classification … on dilated
and attention-guided residual learning for the task of mammographic density classification. …

… classification performance of TA-CNN-NoAttention in comparison with that of LeNet-BN
demonstrates that making full use of multi-view … extracted features by dilated convolutional layers. …

… or multi-view mammography, the classification accuracy will be … [30] adopted a novel
attention-guided dense-upsampling … ResNet introduces deep residual learning to make the …

… a dilation-based and attention-guided residual learning … To improve the performance of
breast density classification … based on multi-view fusion for breast density classification. The …

… Our aim has been to develop a robust multi-view approach for mammogram density
classification by leveraging established evidential theory [34]. While the variational Dirichlet …

… [37], they designed a Multi-View Feature Fusion method that … attention-guided residual
learning to better classify density. … breast density classification into the breast abnormal/normal …

… To take full advantage of multi-view information, we propose a novel pure transformer
based multi-view network to solve the question of mammographic image classification. In our …

… In practice, the proposed multi-view framework can be applied to any given view as the main
image, and we apply it to all available views to obtain the mass detection and classification …

… of image classification. Then, … of classification. The investigational results proved the
implemented model could enhance the treatment power by the classification and detection of breast …

… on dilated and attention-guided residual learning for the mammography density classification
task. In … [9] proposed a multi-view three-layer CNN to categorize breast density into the four …