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Table 1 AUC performance of unsupervised anomaly detection on T1 scans using average \(\ell _2\) loss (among whole slice sets/continuous 10 slice sets exhibiting the highest loss) per scan. Unchanged CDR = 0 (i.e., cognitively healthy population) is compared against: (i) all the other CDRs (i.e., dementia); (ii) CDR = 0.5 (i.e., very mild dementia); (iii) CDR = 1 (i.e., mild dementia); (iv) CDR = 2 (i.e., moderate dementia). Each model is trained for 1.8M steps

From: MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction

CDR = 0 versus CDR = 0.5 + 1 + 2 CDR = 0.5 CDR = 1 CDR = 2
MADGAN 0.768 0.750 0.797 0.829
MADGAN (10 slice sets) 0.764 0.745 0.793 0.830
MADGAN w/o \(\ell _1\) Loss 0.693 0.689 0.699 0.711
MADGAN w/o \(\ell _1\) Loss (10 slice sets) 0.705 0.697 0.717 0.736
3-SA MADGAN 0.752 0.736 0.775 0.835
3-SA MADGAN (10 slice sets) 0.739 0.725 0.760 0.810
3-SA MADGAN w/o \(\ell _1\) Loss 0.728 0.715 0.748 0.785
3-SA MADGAN w/o \(\ell _1\) Loss (10 slice sets) 0.735 0.721 0.756 0.806
7-SA MADGAN 0.765 0.743 0.800 0.832
7-SA MADGAN (10 slice sets) 0.764 0.743 0.798 0.835
7-SA MADGAN w/o \(\ell _1\) Loss 0.759 0.727 0.809 0.894
7-SA MADGAN w/o \(\ell _1\) Loss (10 slice sets) 0.746 0.710 0.803 0.868