Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

by   Ashay Patel, et al.

Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous, it is challenging to train general-purpose discriminative cancer detection models, with data availability and disease complexity often cited as a limiting factor. Unsupervised anomaly detection models have been suggested as a putative solution. These models learn a healthy representation of tissue and detect cancer by predicting deviations from the healthy norm, which requires models capable of accurately learning long-range interactions between organs and their imaging patterns with high levels of expressivity. Such characteristics are suitably satisfied by transformers, which have been shown to generate state-of-the-art results in unsupervised anomaly detection by training on normal data. This work expands upon such approaches by introducing multi-modal conditioning of the transformer via cross-attention i.e. supplying anatomical reference from paired CT. Using 294 whole-body PET/CT samples, we show that our anomaly detection method is robust and capable of achieving accurate cancer localization results even in cases where normal training data is unavailable. In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach with limited training data. Lastly, we propose to combine model uncertainty with a new kernel density estimation approach, and show that it provides clinically and statistically significant improvements when compared to the classic residual-based anomaly maps. Overall, a superior performance is demonstrated against leading state-of-the-art alternatives, drawing attention to the potential of these approaches.


page 2

page 9

page 18

page 20


AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection

Robust and accurate detection and segmentation of heterogenous tumors ap...

Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information

Anomaly detection in medical imaging is to distinguish the relevant biom...

A Discriminative Framework for Anomaly Detection in Large Videos

We address an anomaly detection setting in which training sequences are ...

AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection

Unsupervised anomaly detection is coming into the spotlight these days i...

Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds

Use of an autoencoder (AE) as a normal model is a state-of-the-art techn...

Safe Robot Navigation via Multi-Modal Anomaly Detection

Navigation in natural outdoor environments requires a robust and reliabl...

cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations

Classification of heterogeneous diseases is challenging due to their com...

Please sign up or login with your details

Forgot password? Click here to reset