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

ABSTRACT: Cancers can have highly heterogeneous uptake patterns best visualised in positron emission tomography. These patterns are essential to detect, diagnose, stage and predict the evolution of cancer. Due to this heterogeneity, a general-purpose cancer detection model can be built using unsupervised learning anomaly detection models; these models learn a healthy representation of tissue and detect cancer by predicting deviations from healthy appearances. This task alone requires models capable of accurately learning long-range interactions between organs, imaging patterns, and other abstract features with high levels of expressivity. Such characteristics are suitably satisfied by transformers, and have been shown to generate state-of-the-art results in unsupervised anomaly detection by training

SUBMITTER: Patel A 

PROVIDER: S-EPMC7616582 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature