Fuzzy Attention-Based Border Rendering Orthogonal Network for Lung Organ Segmentation
Bibliographic record
Abstract
Automatic lung organ segmentation on computerized tomography images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negative/positive and leakage issues in numerous state-of-the-art methods. In addition, some lung organs are easily lost during therecycleddown/up-sample procedure, e.g., bronchioles and arterioles, which can cause severe discontinuity issue. Inspired by these, this article introduces an effective lung organ segmentation method called fuzzy attention-based border rendering feature orthogonal network, which 1) integrates an efficient transformer-like fuzzy-attention module into deep networks to cope with the uncertainty in feature representations; 2) decouples and depicts the lung organ regions as cube-trees by focusing only onrecycle-sampling border vulnerable points, rendering the severely discontinuous, false-negative/positive organ regions with two novel global-local cube-tree fusion and sparse patched feature orthogonal modules; 3) develops a multiscale self-knowledge guidance module to improve model performance and robustness. We have demonstrated the efficacy of proposed method on five challenging datasets of lung organ segmentation, i.e., airway and artery. All experimental results demonstrate that our method can achieve the favorable performance significantly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".