arXiv (All)
open access
A Decision-Aware Evaluation Protocol for Uncertainty in Medical Image Segmentation
Medical segmentation uncertainty is often evaluated with calibration error or pixel-level error-detection scores, but these metrics do not show whether uncertainty improves downstream review decisions. We propose a decision-aware evaluation protocol that separates uncertainty estimation from deferra
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Label tree semantic losses for rich multi-class medical image segmentation.#330766
- Decision-Aligned Evaluation of Uncertainty Quantification#310865
- Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?#196156
- Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net#10379
- Navigating Autonomy: Enhancing Accuracy in Medical Decision-making Capacity Assessment.#1251783
Record · ID 1357271
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive