A Weakly Supervised-Guided Soft Attention Network for Classification of Intracranial Hemorrhage

Intracranial hemorrhage (ICH) classification from computed tomography (CT) scans is important for accurate diagnosis of stroke in emergency centers. However, it is challenged by low image contrast and the complex appearance of the lesion. Therefore, the diagnosis results may vary from doctor to doctor under different situations. Existing auxiliary diagnosis algorithms treat the features of each location as equally important, and they do not impose any constraints on the learned features. The learned features used for classification in the model contain noise or features that have no diagnostic significance, which greatly limits the performance and reliability of systems. To facilitate the ICH treatment, we propose to use the auxiliary weak-segmentation-label supervision in multiscale CNN to classify ICH lesions on brain CT images, where the ICH lesions are segmented and used to guide the attention of the classification network. The weak segmentation labels we use are generated by an unsupervised algorithm and do not require additional annotations. We conduct experiments on the RSNA ICH detection data set, and the results demonstrate that our method can achieve an average ACC of 98.1% and an average
$F1$
score of 74.6% on the test set of nearly 80 000 CT images. The proposed weakly supervised-guided attention mechanism can accurately activate those neurons related to diagnosis, while inhibiting the activation of irrelevant areas and noise, thus achieving good performance.