title: RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth
    Segmentation in CBCT
summary: Semi-supervised learning has become a compelling approach for 3D tooth
  segmentation from CBCT scans, where labeled data is minimal. However, existing
  methods still face two persistent challenges: limited corrective supervision in
  structurally ambiguous or mislabeled regions during supervised training and
  performance degradation caused by unreliable pseudo-labels on unlabeled data.
  To address these problems, we propose Region-Aware Instructive Learning (RAIL),
  a dual-group dual-student, semi-supervised framework. Each group contains two
  student models guided by a shared teacher network. By alternating training
  between the two groups, RAIL promotes intergroup knowledge transfer and
  collaborative region-aware instruction while reducing overfitting to the
  characteristics of any single model. Specifically, RAIL introduces two
  instructive mechanisms. Disagreement-Focused Supervision (DFS) Controller
  improves supervised learning by instructing predictions only within areas where
  student outputs diverge from both ground truth and the best student, thereby
  concentrating supervision on structurally ambiguous or mislabeled areas. In the
  unsupervised phase, Confidence-Aware Learning (CAL) Modulator reinforces
  agreement in regions with high model certainty while reducing the effect of
  low-confidence predictions during training. This helps prevent our model from
  learning unstable patterns and improves the overall reliability of
  pseudo-labels. Extensive experiments on four CBCT tooth segmentation datasets
  show that RAIL surpasses state-of-the-art methods under limited annotation. Our
  code will be available at https://github.com/Tournesol-Saturday/RAIL.
paper_authors._id: 681c0409ecb9979e658951f3
paper_authors.name: Chuyu Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:08.240Z
paper_authors._id: 681bfd5a7a587a523b07c587
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/4W3HskyYW_z-LBkX3upDM.jpeg
paper_authors.fullname: Chuyu Zhao
paper_authors.type: user
paper_authors.user: Tournesol-Saturday
paper_authors._id: 681c0409ecb9979e658951f4
paper_authors.name: Hao Huang
paper_authors._id: 681c0409ecb9979e658951f5
paper_authors.name: Jiashuo Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:59:37.395Z
paper_authors._id: 676c21af021be0726d06fa85
paper_authors.avatarUrl: /avatars/6664c701cf2879543ae5de30e31ac424.svg
paper_authors.fullname: GuoJiashuo
paper_authors.type: user
paper_authors.user: gjsinfluence
paper_authors._id: 681c0409ecb9979e658951f6
paper_authors.name: Ziyu Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:59:26.523Z
paper_authors._id: 638c16c51987d67b34103102
paper_authors.avatarUrl: /avatars/329d3939691262897e3d66de2ae16c22.svg
paper_authors.fullname: Ziyu Shen
paper_authors.type: user
paper_authors.user: szyu345
paper_authors._id: 681c0409ecb9979e658951f7
paper_authors.name: Zhongwei Zhou
paper_authors._id: 681c0409ecb9979e658951f8
paper_authors.name: Jie Liu
paper_authors._id: 681c0409ecb9979e658951f9
paper_authors.name: Zekuan Yu
paper_title: RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth
    Segmentation in CBCT
paper_summary: Semi-supervised learning has become a compelling approach for 3D tooth
  segmentation from CBCT scans, where labeled data is minimal. However, existing
  methods still face two persistent challenges: limited corrective supervision in
  structurally ambiguous or mislabeled regions during supervised training and
  performance degradation caused by unreliable pseudo-labels on unlabeled data.
  To address these problems, we propose Region-Aware Instructive Learning (RAIL),
  a dual-group dual-student, semi-supervised framework. Each group contains two
  student models guided by a shared teacher network. By alternating training
  between the two groups, RAIL promotes intergroup knowledge transfer and
  collaborative region-aware instruction while reducing overfitting to the
  characteristics of any single model. Specifically, RAIL introduces two
  instructive mechanisms. Disagreement-Focused Supervision (DFS) Controller
  improves supervised learning by instructing predictions only within areas where
  student outputs diverge from both ground truth and the best student, thereby
  concentrating supervision on structurally ambiguous or mislabeled areas. In the
  unsupervised phase, Confidence-Aware Learning (CAL) Modulator reinforces
  agreement in regions with high model certainty while reducing the effect of
  low-confidence predictions during training. This helps prevent our model from
  learning unstable patterns and improves the overall reliability of
  pseudo-labels. Extensive experiments on four CBCT tooth segmentation datasets
  show that RAIL surpasses state-of-the-art methods under limited annotation. Our
  code will be available at https://github.com/Tournesol-Saturday/RAIL.
paper_ai_summary: RAIL, a dual-group semi-supervised learning framework with Region-Aware Instructive Learning, improves 3D tooth segmentation by addressing supervision and pseudo-label challenges in CBCT scans.
paper_ai_keywords: semi-supervised learning 3D tooth segmentation CBCT scans Region-Aware Instructive Learning dual-group dual-student framework Disagreement-Focused Supervision Confidence-Aware Learning intergroup knowledge transfer supervised learning unsupervised phase pseudo-labels