Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Evi M. C. Huijben Sina Amirrajab Josien P. W. Pluim
Out-of-distribution (OOD) detection is crucial for the safety and reliability
of artificial intelligence algorithms, especially in the medical domain. In the
context of the Medical OOD (MOOD) detection challenge 2023, we propose a
pipeline that combines a histogram-based method and a diffusion-based method.
The histogram-based method is designed to accurately detect homogeneous
anomalies in the toy examples of the challenge, such as blobs with constant
intensity values. The diffusion-based method is based on one of the latest
methods for unsupervised anomaly detection, called DDPM-OOD. We explore this
method and propose extensive post-processing steps for pixel-level and
sample-level anomaly detection on brain MRI and abdominal CT data provided by
the challenge. Our results show that the proposed DDPM method is sensitive to
blur and bias field samples, but faces challenges with anatomical deformation,
black slice, and swapped patches. These findings suggest that further research
is needed to improve the performance of DDPM for OOD detection in medical
images.