In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging real-world dataset of Chest X-ray images. We experimentally evaluate our method on the Medical Out-of-Distribution Analysis Challenge 2021.
@article{arxiv.2201.09579,
title = {AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation},
author = {Felix Meissen and Georgios Kaissis and Daniel Rueckert},
journal= {arXiv preprint arXiv:2201.09579},
year = {2022}
}
Comments
8 pages, 3 figures, part of the MICCAI MOOD Challenge 2021