We propose a novel unsupervised out-of-distribution detection method for
medical images based on implicit fields image representations. In our approach,
an auto-decoder feed-forward neural network learns the distribution of healthy
images in the form of a mapping between spatial coordinates and probabilities
over a proxy for tissue types. At inference time, the learnt distribution is
used to retrieve, from a given test image, a restoration, i.e. an image
maximally consistent with the input one but belonging to the healthy
distribution. Anomalies are localized using the voxel-wise probability
predicted by our model for the restored image. We tested our approach in the
task of unsupervised localization of gliomas on brain MR images and compared it
to several other VAE-based anomaly detection methods. Results show that the
proposed technique substantially outperforms them (average DICE 0.640 vs 0.518
for the best performing VAE-based alternative) while also requiring
considerably less computing time.

提出了一种基于隐式场图像表示的医学图像无监督的脱离分布检测方法，应用于脑 MR 图像中的胶质瘤本地化任务，结果表明该方法显著优于其他基于 VAE 的异常检测方法（平均 DICE 0.640 vs 0.518），同时需要更少的计算时间。