474 research outputs found
The role of noise in denoising models for anomaly detection in medical images
Pathological brain lesions exhibit diverse appearance in brain images, in
terms of intensity, texture, shape, size, and location. Comprehensive sets of
data and annotations are difficult to acquire. Therefore, unsupervised anomaly
detection approaches have been proposed using only normal data for training,
with the aim of detecting outlier anomalous voxels at test time. Denoising
methods, for instance classical denoising autoencoders (DAEs) and more recently
emerging diffusion models, are a promising approach, however naive application
of pixelwise noise leads to poor anomaly detection performance. We show that
optimization of the spatial resolution and magnitude of the noise improves the
performance of different model training regimes, with similar noise parameter
adjustments giving good performance for both DAEs and diffusion models. Visual
inspection of the reconstructions suggests that the training noise influences
the trade-off between the extent of the detail that is reconstructed and the
extent of erasure of anomalies, both of which contribute to better anomaly
detection performance. We validate our findings on two real-world datasets
(tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain
CT), showing good detection on diverse anomaly appearances. Overall, we find
that a DAE trained with coarse noise is a fast and simple method that gives
state-of-the-art accuracy. Diffusion models applied to anomaly detection are as
yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202
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