Table 2 Evaluation results for different choices of hyper-parameters, measured in terms of Segmentation Quality (SQ) and Recognition Quality (RQ), with training and inference times.
From: High-throughput segmentation of unmyelinated axons by deep learning
| Â | SQ | RQ | Training (min.) | Inference (s) |
|---|---|---|---|---|
Network depth | ||||
5 | 0.753 | 0.778 | 346 | 187 |
4 | 0.757 | 0.816 | 269 | 152 |
3 | 0.781 | 0.616 | 219 | 119 |
2 | 0.758 | 0.214 176 | 89 | Â |
Loss | ||||
Weighted CE | 0.757 | 0.816 | 269 | 152 |
Generalized dice | 0.324 | 0.184 | 276 | 151 |
Focal | 0.756 | 0.619 | 262 | 151 |
No border class | 0.318 | 0.150 | 262 | 150 |
Tile size | ||||
256 | 0.769 | 0.647 | 79 | 250 |
384 | 0.767 | 0.756 | 169 | 216 |
512 | 0.757 | 0.816 | 269 | 152 |
524 without padding | 0.766 | 0.729 | 227 | 4.5 |
768 | 0.769 | 0.733 | 332\(^{\dagger }\) | 54\(^{\dagger }\) |
Tile sampling | ||||
Area-based | 0.757 | 0.816 | 269 | 152 |
Random | 0.759 | 0.742 | 270 | 150 |
Fiber-centered | 0.786 | 0.663 | 213 | 148 |
Proportional | 0.783 | 0.675 | 213 | 150 |
