Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation

2026-01-12 · Bing Yu, Liu Shi, Haitao Wang, Deran Qi, Xiang Cai, Wei Zhong, Qiegen Liu arxiv

Accurate three-dimensional (3D) tooth segmentation from Cone-Beam Computed Tomography (CBCT) is a prerequisite for digital dental workflows. However, achieving high-fidelity segmentation remains challenging due to adhesion artifacts in naturally occluded scans, which are caused by low contrast and indistinct inter-arch boundaries. To address these limitations, we propose the Anatomy Aware Cascade Network (AACNet), a coarse-to-fine framework designed to resolve boundary ambiguity while maintaining global structural consistency. Specifically, we introduce two mechanisms: the Ambiguity Gated Boundary Refiner (AGBR) and the Signed Distance Map guided Anatomical Attention (SDMAA). The AGBR employs an entropy based gating mechanism to perform targeted feature rectification in high uncertainty transition zones. Meanwhile, the SDMAA integrates implicit geometric constraints via signed distance map to enforce topological consistency, preventing the loss of spatial details associated with standard pooling. Experimental results on a dataset of 125 CBCT volumes demonstrate that AACNet achieves a Dice Similarity Coefficient of 90.17 \% and a 95\% Hausdorff Distance of 3.63 mm, significantly outperforming state-of-the-art methods. Furthermore, the model exhibits strong generalization on an external dataset with an HD95 of 2.19 mm, validating its reliability for downstream clinical applications such as surgical planning. Code for AACNet is available at https://github.com/shiliu0114/AACNet.

📄 PDF Abstract BibTeX arXiv:2601.07499

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CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support

2026-05-19 · Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel arxiv

Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalen…

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates

2026-02-24 · Haoze Song, Zhihao Li, Mengyi Deng, Xin Li 외 arxiv

Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncertainty due to finite data, imperfect opti…

A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty

2024-01-05 · Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis

Exploration is a significant challenge in practical reinforcement learning (RL), and uncertainty-aware exploration that incorporates the quantification of epistemic and aleatory uncertainty has been recognized as an effe…

Decision Making Reinforcement Learning (RL)

Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing

2025-04-16 · Ilkin Sevgi Isler, David Mohaisen, Curtis Lisle, Damla Turgut 외

Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertainty-guided, coarse-to-fine segmentation f…

Anatomy Computed Tomography (CT)Segmentation Tumor Segmentation

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience

2026-06-09 · Jia Luo arxiv

AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforcement learning (RL) optimizes flows but is semantically blind to uns…

Reinforcement Learning Boundary Detection