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Implicit field learning for unsupervised anomaly detection in medical images

Marimont, S. N. & Tarroni, G. ORCID: 0000-0002-0341-6138 (2021). Implicit field learning for unsupervised anomaly detection in medical images. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Lecture Notes in Computer Science, 12902. . UNSPECIFIED. ISBN 978-3-030-87195-6 doi: 10.1007/978-3-030-87196-3_18


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.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: Accepted for publication in MICCAI 2021
Publisher Keywords: anomaly detection, unsupervised learning, implicit fields, occupancy networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Departments: School of Science & Technology > Computer Science
Text - Accepted Version
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