Table 4 Performance of our framework for NC vs. AD, subcortical vascular disease with no cognitive impairment (NCI) vs. subcortical vascular mild cognitive impairment (svMCI), and methylated MGMT (MGMT+) vs. unmethylated MGMT (MGMT-) classifications

From: Achieving multi-modal brain disease diagnosis performance using only single-modal images through generative AI

Methods

AUC

ACC

Sensitivity

Specificity

F1-score

NC vs. AD on OASIS-3 (pretrained on ADNI)

3D CNN (SI)

0.852 ± 0.020

0.772 ± 0.014

0.710 ± 0.091

0.826 ± 0.103

0.743 ± 0.014

3D CNN (MI)

0.885 ± 0.003

0.821 ± 0.007

0.719 ± 0.023

0.912 ± 0.013

0.790 ± 0.011

Ours3DCNN (SI+MmFE)

0.890 ± 0.005

0.831 ± 0.004

0.752 ± 0.024

0.900 ± 0.018

0.806 ± 0.008

NC vs. AD on HS Hospital (pretrained on ADNI)

3D CNN (SI)

0.751 ± 0.081

0.718 ± 0.038

0.639 ± 0.067

0.774 ± 0.043

0.639 ± 0.055

3D CNN (MI)

0.863 ± 0.076

0.839 ± 0.043

0.754 ± 0.086

0.912 ± 0.043

0.778 ± 0.046

Ours3DCNN (SI+MmFE)

0.849 ± 0.098

0.783 ± 0.078

0.734 ± 0.085

0.810 ± 0.106

0.730 ± 0.080

NCI vs. svMCI on RJ Hospital

3D CNN (SI)

0.677 ± 0.036

0.649 ± 0.034

0.634 ± 0.079

0.676 ± 0.099

0.670 ± 0.057

3D CNN (MI)

0.713 ± 0.041

0.676 ± 0.064

0.697 ± 0.066

0.680 ± 0.149

0.708 ± 0.066

Ours3DCNN (SI+MmFE)

0.705 ± 0.040

0.660 ± 0.051

0.684 ± 0.105

0.662 ± 0.095

0.693 ± 0.057

MGMT+ vs. MGMT- on BraTS 2021

3D CNN (SI)

0.574 ± 0.036

0.564 ± 0.022

0.539 ± 0.035

0.591 ± 0.033

0.562 ± 0.025

3D CNN (MI)

0.594 ± 0.030

0.581 ± 0.034

0.602 ± 0.057

0.556 ± 0.029

0.598 ± 0.052

Ours3DCNN (SI+MmFE)

0.600 ± 0.023

0.593 ± 0.025

0.634 ± 0.060

0.544 ± 0.074

0.616 ± 0.048

  1. We show the mean value and standard deviation of the 5-fold results. SI Single-modal input, MI Multi-modal input, MmFE Multi-modal feature enhanced.