  Ischemic stroke is a severe condition caused by the blockage of brain blood
vessels, and can lead to the death of brain tissue due to oxygen deprivation.
Thrombectomy has become a common treatment choice for ischemic stroke due to
its immediate effectiveness. But, it carries the risk of postoperative cerebral
hemorrhage. Clinically, multiple CT scans within 0-72 hours post-surgery are
used to monitor for hemorrhage. However, this approach exposes radiation dose
to patients, and may delay the detection of cerebral hemorrhage. To address
this dilemma, we propose a novel prediction framework for measuring
postoperative cerebral hemorrhage using only the patient's initial CT scan.
Specifically, we introduce a dual-task mutual learning framework to takes the
initial CT scan as input and simultaneously estimates both the follow-up CT
scan and prognostic label to predict the occurrence of postoperative cerebral
hemorrhage. Our proposed framework incorporates two attention mechanisms, i.e.,
self-attention and interactive attention. Specifically, the self-attention
mechanism allows the model to focus more on high-density areas in the image,
which are critical for diagnosis (i.e., potential hemorrhage areas). The
interactive attention mechanism further models the dependencies between the
interrelated generation and classification tasks, enabling both tasks to
perform better than the case when conducted individually. Validated on clinical
data, our method can generate follow-up CT scans better than state-of-the-art
methods, and achieves an accuracy of 86.37% in predicting follow-up prognostic
labels. Thus, our work thus contributes to the timely screening of
post-thrombectomy cerebral hemorrhage, and could significantly reform the
clinical process of thrombectomy and other similar operations related to
stroke.
