Harder synthetic anomalies to improve OoD detection in Medical Images

Our method builds upon previous Medical Out-of-Distribution (MOOD) challenge winners that empirically show that synthetic local anomalies generated copying / interpolating foreign patches are useful to train segmentation networks able to generalize to unseen types of anomalies. In terms of the synthetic anomaly generation process, our contributions makes synthetic anomalies more heterogeneous and challenging by 1) using random shapes instead of squares and 2) smoothing the interpolation edge of anomalies so networks cannot rely on the high gradient between image - foreign patch to identify anomalies. Our experiments using the validation set of 2020 MOOD winners show that both contributions improved substantially the method performance. We used a standard 3D U-Net architecture as segmentation network, trained patch-wise in both brain and abdominal datasets. Our final challenge submission consisted of 10 U-Nets trained across 5 data folds with different configurations of the anomaly generation process. Our method achieved first position in both sample-wise and pixel-wise tasks in the 2022 edition of the Medical Out-of-Distribution held at MICCAI.