Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT

arXiv:2609.37386 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://nubagcilab.github.io/BronchoAccess

Terminology

• AUROC — 17× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• AUC — 1× in this paper · explained in 2 episodes
• AnatomyAware Mixture-of-Experts — 1× in this paper
• D Euclidean Distance Transform — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• MONAI: An open-source framework for deep learning in healthcare
• Semi-Supervised Classification with Graph Convolutional Networks
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Graph Attention Networks
• NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline
• How Powerful are Graph Neural Networks?

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