AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation
This work addresses pathology segmentation in medical imaging, offering a novel approach that could enhance diagnostic tools, though it appears incremental as it builds on existing anomaly detection methods.
The paper tackles the problem of sub-optimal inductive biases in unsupervised pathology segmentation by proposing AutoSeg, which generates artificial anomalies to improve detection, achieving superior performance on a Chest X-ray dataset from the Medical Out-of-Distribution Analysis Challenge 2021.
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.