RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth
  Segmentation in CBCT
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.
681c0409ecb9979e658951f3
Chuyu Zhao
claimed_verified
2025-05-08T08:59:08.240Z
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Chuyu Zhao
user
Tournesol-Saturday
681c0409ecb9979e658951f4
Hao Huang
681c0409ecb9979e658951f5
Jiashuo Guo
admin_assigned
2025-05-14T12:59:37.395Z
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GuoJiashuo
user
gjsinfluence
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Ziyu Shen
admin_assigned
2025-05-14T12:59:26.523Z
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/avatars/329d3939691262897e3d66de2ae16c22.svg
Ziyu Shen
user
szyu345
681c0409ecb9979e658951f7
Zhongwei Zhou
681c0409ecb9979e658951f8
Jie Liu
681c0409ecb9979e658951f9
Zekuan Yu
RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth
  Segmentation in CBCT
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.
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.
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