Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach--which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting--is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
翻译:悬浮计数是疾病严重程度、病人预测和治疗效果的重要指标,但在医学成像中作为任务计数往往被偏向于分化。 这项工作引入了一种新的、可不断区分的功能,即以一致的方式绘制损害分解预测图以显示损害计概率分布。 拟议的端到端方法包括 voxel 群集、 腐蚀水平 voxel 概率集合, 和 Poisson- binomial计数非参数化计数, 从而提供了一种强有力和一致的方法, 以临时计数能力来增加折损分解模型。 有关加多利翁- 增减损计数的实验表明,我们的方法产生准确和有条理的计数分布,能够捕捉有意义的不确定信息。 这些实验还表明,我们的模型在低数据体系中适合多任务地学习折损分解,在低数据体系中是有效的,并且能够抵御对抗性攻击。