Research map / Probabilistic and scientific modeling / Molecules and proteins
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use selective inference homotopy method instead of permutation test | DNN segmentation inference · Brain image dataset | FPR: 0.640 -> 0.057 | Helped |
| use selective p-value instead of naive p-value on tumor-free images | DNN segmentation inference · Brain images without tumor | p-value: 0.00 -> 0.67 | Helped |
| use homotopy method instead of over-conditioning (ablation) | Proposed SI · synthetic images n=256 | TPR | Helped |
| use selective p-value instead of naive p-value in synthetic null images | DNN segmentation inference · synthetic null images | FPR | Helped |
| apply method to Laplace, skew normal and t20 noise and estimated variance | Proposed SI · synthetic images | FPR | No clear effect |
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