SALM: A Unified Model for 2D and 3D Region of Interest Segmentation in Lung CT Scans Using Vision Transformers
Bibliographic record
Abstract
Accurate segmentation of Regions of Interest (ROI) in lung Computed Tomography (CT) is crucial for early lung cancer diagnosis and treatment planning. However, the variability in size, shape, and location of lung lesions, along with the complexity of 3D spatial relationships, poses significant challenges. In this work, we propose SALM (Segment Anything in Lung Model), a deep learning model for 2D and 3D ROI segmentation. SALM leverages Vision Transformers, proposing an adaptation of positional encoding functions to effectively capture spatial relationships in both 2D slices and 3D volumes using a single, unified model. Evaluation on the LUNA16 dataset demonstrated strong performance in both modalities. In 2D segmentation, SALM achieved a Dice score of 93% on 124,662 slices. For 3D segmentation using 174 3D images from the same dataset, SALM attained a Dice score of 81.88%. We also tested SALM on an external database (PleThora) on a subset of 255 pulmonary CT from diseased patients, where it achieved a Dice score of 78.82%. These results highlight SALM’s ability to accurately segment lung ROI in both 2D and 3D, demonstrating its potential to improve the accuracy and efficiency of computer-aided diagnosis for lung cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".