Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the U-Net segmentation architecture | AmnioML · Fetal MRI test set | average Dice coefficient: 0.91 ± 0.06; tied for best | Helped |
| Use thresholded volume prediction (Algorithm 1) instead of standard split conformal prediction | AmnioML volume intervals · Fetal MRI test set | average interval length | Helped |
| Use thresholded volume prediction (Algorithm 1) | AmnioML volume intervals · Fetal MRI test set | empirical coverage | No clear effect |
| Add neighboring slices as motion-context features (ablation) | U-Net slice segmentation · Validation set | prediction performance | No clear effect |
| Use three input slices as motion context (ablation) | U-Net slice segmentation · Validation set | prediction performance | Helped |
| Use AmnioML to assist manual segmentation | Medical specialists · Clinical use | segmentation time speedup: 20.7 times | Helped |
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