Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories

Lemar Abdi, Francisco Caetano, Amaan Valiuddin, Christiaan Viviers, hamdi joudeh, Fons van der Sommen

Keywords: out-of-distribution detection, Diffusion model, data imbalance

Abstract:

In medical imaging, unsupervised out-of-distribution (OOD)
detection offers an attractive approach for identifying pathological cases
with extremely low incidence rates. In contrast to supervised methods,
OOD-based approaches function without labels and are inherently robust
to data imbalances. Current generative approaches often rely on
likelihood estimation or reconstruction error, but these methods can be
computationally expensive, unreliable, and require retraining if the inlier
data changes. These limitations hinder their ability to distinguish nominal
from anomalous inputs efficiently, consistently, and robustly. We
propose a reconstruction-free OOD detection method that leverages the
forward diffusion trajectories of a Stein score-based denoising diffusion
model (SBDDM). By capturing trajectory curvature via the estimated
Stein score, our approach enables accurate anomaly scoring with only
five diffusion steps. A single SBDDM pre-trained on a large, semantically
aligned medical dataset generalizes effectively across multiple Near-
OOD and Far-OOD benchmarks, achieving state-of-the-art performance
while drastically reducing computational cost during inference. Compared
to existing methods, SBDDM achieves a relative improvement of
up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making
it a practical building block for real-time, reliable computer-aided
diagnosis.

Submission Number: 9