Hinrichs et al. [9] |
Conventional classifiers (i.e., LPboosting, SVM) + voxel-level engineered features |
183 (AD+NC) |
0.82 |
0.85 |
0.80 |
0.88 |
- |
- |
- |
- |
Salvatore et al. [41] |
162 NC + 76 sMCI + 134 pMCI + 137 AD |
0.76 |
- |
- |
- |
0.66 |
- |
- |
- |
Koikkalainen et al. [45] |
Conventional classifiers (i.e., linear regression, ensemble SVM) + region-level engineered features |
115 NC + 115 sMCI + 54 pMCI + 88 AD |
0.86 |
0.81 |
0.91 |
- |
0.72 |
0.77 |
0.71 |
- |
Liu et al. [17] |
128 NC + 117 sMCI + 117 pMCI + 97 AD |
0.93 |
0.95 |
0.90 |
0.96 |
0.79 |
0.88 |
0.76 |
0.83 |
Coupé et al. [19] |
Conventional classifiers (i.e., linear discriminant analysis, hierarchical SVM, MIL model) + patch-level engineered features |
231 NC + 238 sMCI + 167 pMCI + 198 AD |
0.91 |
0.87 |
0.94 |
- |
0.74 |
0.73 |
0.74 |
- |
Liu et al. [21] |
229 NC + 198 AD |
0.92 |
0.91 |
0.93 |
0.95 |
- |
- |
- |
- |
Tong et al. [22] |
231 NC + 238 sMCI + 167 pMCI + 198 AD |
0.90 |
0.86 |
0.93 |
- |
0.72 |
0.69 |
0.74 |
- |
Suk et al. [50] |
Deep Boltzmann machine [49] + patch-level engineered features |
101 NC + 128 sMCI + 76 pMCI + 93 AD |
0.92 |
0.92 |
0.95 |
0.97 |
0.72 |
0.37 |
0.91 |
0.73 |
Liu et al. [51] |
Stacked auto-encoders [52] + region-level engineered features |
204 NC + 180 AD |
0.79 |
0.83 |
0.87 |
0.78 |
- |
- |
- |
- |
Shi et al. [66] |
Deep polynomial network [67] + region-level engineered features |
52 NC + 56 sMCI + 43 pMCI + 51 AD |
0.95 |
0.94 |
0.96 |
0.96 |
0.75 |
0.63 |
0.85 |
0.72 |
Korolev et al. [68] |
CNN + whole brain sMRI |
61 NC + 77 sMCI + 43 pMCI + 50 AD |
0.80 |
- |
- |
0.87 |
0.52 |
- |
- |
0.52 |
Khvostikov et al. [33] |
CNN + hippocampal sMRI |
58 NC + 48 AD |
0.85 |
0.88 |
0.90 |
- |
- |
- |
- |
- |
Our wH-FCN mehtod |
Hierarchical FCN + automatic discriminative localizatoin |
429 NC + 465 sMCI + 205 pMCI + 358 AD |
0.90 |
0.82 |
0.97 |
0.95 |
0.81 |
0.53 |
0.85 |
0.78 |