论文标题
不确定性寻找新颖性
Finding novelty with uncertainty
论文作者
论文摘要
医学图像通常用于检测和表征病理和疾病。但是，在医学图像中自动识别和分割病理学是具有挑战性的，因为跨疾病的病理出现差异很大。为了应对这一挑战，我们提出了一种贝叶斯深度学习方法，该方法学会将健康的计算机断层扫描图像转换为磁共振图像，并同时计算体素的不确定性。由于图像的病理区域中发生了高不确定性，因此这种不确定性可用于无监督的异常分割。我们通过将两种类型的不确定性结合到我们称为Scibilic不确定性的新数量中，对无监督的异常分割任务展示了令人鼓舞的实验结果。
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show encouraging experimental results on an unsupervised anomaly segmentation task by combining two types of uncertainty into a novel quantity we call scibilic uncertainty.
