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paper CBAM: Convolutional Block Attention Module We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, arxiv.org code

When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide wi Background Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the a bmcmedresmetho..

paper An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural ne… www.sciencedirect.com code GitHub - nyukat/GMIC: An interpretable classifier for high-resolution breast cancer screening..

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses arxiv.org Figure 2: Accuracies and ex..