Introduction
Novel contribution
Methods
Data
Pipeline
Definition of labels for regression
Machine learning algorithms
Models
Training
Inference
Experiment design and validation
Implementation
Results
Data
Model validation
Model | HcF | E2E | |||||||
---|---|---|---|---|---|---|---|---|---|
Experiment | N/A | T1w | GIF | CWD | |||||
Label | MC dropout | No | Yes | No | Yes | No | Yes | No | Yes |
lu | sfg | 3.09 (0.47) | 3.08 (0.48) | 0.43 (1.09) | 0.35 (1.02) | 0.36 (1.03) | 0.35 (1.02) | 0.23 (0.64) | 0.20 (0.57) |
mfg | 4.26 (0.98) | 4.28 (0.97) | 0.43 (1.01) | 0.41 (0.98) | 0.70 (1.85) | 0.70 (1.84) | 0.43 (1.03) | 0.41 (0.99) | |
ifog | 1.16 (0.30) | 1.12 (0.23) | 0.13 (0.35) | 0.11 (0.30) | 0.44 (1.20) | 0.40 (1.17) | 0.14 (0.38) | 0.10 (0.30) | |
tg | 2.03 (1.31) | 1.99 (1.32) | 0.46 (1.36) | 0.46 (1.35) | 0.47 (1.37) | 0.47 (1.37) | 0.15 (0.39) | 0.13 (0.35) | |
apcg | 3.78 (0.85) | 3.78 (0.83) | 0.14 (0.36) | 0.11 (0.29) | 0.64 (1.51) | 0.53 (1.50) | 0.22 (0.61) | 0.18 (0.49) | |
po | 3.23 (0.33) | 3.26 (0.32) | 0.32 (0.88) | 0.31 (0.86) | 0.65 (1.73) | 0.60 (1.73) | 0.62 (1.78) | 0.62 (1.78) | |
\(\hat{\mathbf{eb }}\) | sfg | 62.50 (42.57) | 62.30 (43.13) | 15.98 (7.31) | 8.14 (7.98) | 87.79 (42.00) | 80.73 (44.00) | 21.14 (8.76) | 14.78 (9.33) |
mfg | 56.38 (11.01) | 56.35 (12.22) | 26.92 (4.75) | 16.30 (2.50) | 71.70 (59.74) | 60.26 (62.74) | 27.34 (4.61) | 17.35 (3.36) | |
ifog | 41.96 (5.05) | 42.08 (7.36) | 25.66 (9.58) | 14.08 (8.35) | 185.58 (123.5) | 175.60 (131.65) | 54.07 (80.68) | 44.72 (83.61) | |
tg | 89.42 (93.12) | 87.39 (95.67) | 24.49 (7.52) | 15.69 (7.19) | 88.12 (98.00) | 79.45 (100.13) | 56.23 (82.55) | 48.07 (84.31) | |
apcg | 72.10 (49.48) | 72.40 (51.52) | 29.22 (20.65) | 17.70 (20.13) | 143.69 (82.28) | 137.87 (86.25) | 24.87 (3.59) | 13.39 (2.24) | |
po | 73.83 (5.64) | 71.70 (6.21) | 36.31 (15.01) | 25.30 (16.44) | 186.13 (91.58) | 178.95 (96.09) | 62.5 (41.5) | 48.42 (34.80) |