Weakly-Supervised Learning for Attention-Guided Skull Fracture Classification In Computed Tomography Imaging

We propose a novel attention-guided deep learning framework for image classification, with the goal of predicting both image and pixel-level labels for input images. While the training images are with either positive or negative labels, we do not assume that each training image is with annotated pixel-level ground truth information, and thus our method can be realized in such a weakly supervised setting. Our proposed module can be easily combined with standard CNN architectures with no extra parameter needed. With the above advantages, we evaluate our model on a skull CT dataset, and the experimental results confirm the effectiveness and robustness of our approach over recent popular CNN architectures.