Research map / Detection and segmentation / Open-set and few-shot detection
Computing Valid P-Values for Image Segmentation by Selective Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Condition inference on the observed segmentation result | PSegI-GC and PSegI-TH · Artificial data, null images | false positive rate | Helped |
| Use naive p-values without accounting for segmentation bias | GC- and TH-based segmentation · Artificial data, null images | false positive rate control | Hurt |
| Condition inference on the observed segmentation result | PSegI-GC and PSegI-TH · Artificial data with differing region means | true positive rate | Helped |
| Use naive p-values without accounting for segmentation bias | GC- and TH-based segmentation · Artificial data with differing region means | false positive rate | Hurt |
| Condition inference on the observed segmentation result | PSegI-GC · Pathological images with and without fibrous regions | significance classification at alpha = 0.05 | Helped |
| Use naive p-values without accounting for segmentation bias | GC-based segmentation · Pathological images with and without fibrous regions | significance classification at alpha = 0.05 | Hurt |
| Condition inference on the observed segmentation result | PSegI-TH · CT images with and without tumor regions | significance classification at alpha = 0.05 | Helped |
| Use naive p-values without accounting for segmentation bias | Local threshold-based segmentation · CT images with and without tumor regions | significance classification at alpha = 0.05 | Hurt |
| Use filtered image features as the test statistic | PSegI-TH · Filtered texture example | selective p-value: selective-p = 0.00 | Helped |
Closest papers by meaning
- Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference (NeurIPS 2022 · similarity 0.90)
- ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation (ICLR 2024 · similarity 0.85)
- Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation (NeurIPS 2026 · similarity 0.85)
- Uncertainty-aware Fine-tuning of Segmentation Foundation Models (NeurIPS 2024 · similarity 0.83)
- Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation (ICML 2025 · similarity 0.83)
- Stratify or Die: Rethinking Data Splits in Image Segmentation (NeurIPS 2025 · similarity 0.83)
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