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Lesion detection in brain Magnetic Resonance Images (MRIs) remains achallenging task. MRIs are typically read and interpreted by domain experts,which is a tedious and time-consuming process. Recently, unsupervised anomalydetection (UAD) in brain MRI with deep learning has shown promising results toprovide a quick, initial assessment. So far, these methods only rely on thevisual appearance of healthy brain anatomy for anomaly detection. Anotherbiomarker for abnormal brain development is the deviation between the brain ageand the chronological age, which is unexplored in combination with UAD. Wepropose deep learning for UAD in 3D brain MRI considering additional ageinformation. We analyze the value of age information during training, as anadditional anomaly score, and systematically study several architectureconcepts. Based on our analysis, we propose a novel deep learning approach forUAD with multi-task age prediction. We use clinical T1-weighted MRIs of 1735healthy subjects and the publicly available BraTs 2019 data set for our study.Our novel approach significantly improves UAD performance with an AUC of 92.60%compared to an AUC-score of 84.37% using previous approaches without ageinformation.

Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction