Lacunes of presumed vascular origin, also referred to as lacunar infarcts,
are important to assess cerebral small vessel disease and cognitive diseases
such as dementia. However, visual rating of lacunes from imaging data is
challenging, time-consuming, and rater-dependent, owing to their small size,
sparsity, and mimics. Whereas recent developments in automatic algorithms have
shown to make the detection of lacunes faster while preserving sensitivity,
they also showed a large number of false positives, which makes them
impractical for use in clinical practice or large-scale studies. Here, we
develop a novel framework that, in addition to lacune detection, outputs a
categorical burden score. This score could provide a more practical estimate of
lacune presence that simplifies and effectively accelerates the imaging
assessment of lacunes. We hypothesize that the combination of detection and the
categorical score makes the procedure less sensitive to noisy labels.

开发一种新框架，用于自动检测小血管病和认知疾病视图中的腔隙性病变，该框架通过输出分类负担评分来简化和有效加速识别过程，并减少对噪声标签的敏感度。