Detecting Outliers with Foreign Patch Interpolation

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.

[1]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[2]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[3]  Alexander Binder,et al.  Deep One-Class Classification , 2018, ICML.

[4]  Ali Razavi,et al.  Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.

[5]  Shadi Albarqouni,et al.  Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI , 2020, MICCAI.

[6]  Dwarikanath Mahapatra,et al.  SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays , 2020, MICCAI.

[7]  Ganapathy Krishnamurthi,et al.  Generative adversarial networks for brain lesion detection , 2017, Medical Imaging.

[8]  Oriol Vinyals,et al.  Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.

[9]  Ran El-Yaniv,et al.  Deep Anomaly Detection Using Geometric Transformations , 2018, NeurIPS.

[10]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[11]  Jinwoo Shin,et al.  CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances , 2020, NeurIPS.

[12]  Longbing Cao,et al.  Deep Learning for Anomaly Detection: A Review , 2020, ArXiv.

[13]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[14]  Nikos Komodakis,et al.  Wide Residual Networks , 2016, BMVC.

[15]  Andrew Gordon Wilson,et al.  Averaging Weights Leads to Wider Optima and Better Generalization , 2018, UAI.

[16]  Klaus H. Maier-Hein,et al.  Unsupervised Anomaly Localization using Variational Auto-Encoders , 2019, MICCAI.

[17]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..