Implicit field learning for unsupervised anomaly detection in medical images
This addresses the problem of detecting anomalies like gliomas in brain MR images for medical diagnosis, offering a novel approach with improved performance and efficiency.
The paper tackles unsupervised anomaly detection in medical images by proposing a method based on implicit fields to learn healthy image distributions, achieving an average DICE score of 0.640 for glioma localization, outperforming VAE-based methods.
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.