CoviSwin: A Deep Vision Transformer for Automatic Segmentation of COVID-19 CT Scans
Bibliographic record
Abstract
Precise segmentation of COVID-19 lesions in chest Computed Tomography (CT) scans can directly impact patient care, yet existing methods struggle, when undertaking this task, with the heterogeneous appearance of ground-glass opacities, consolidations, and the availability of limited labeled data. We propose herein CoviSwin, a Transformer-based U-shaped encoder-decoder network that combines the Large model of Swin Transformer Version 2 with attention and residual connections to capture both global context and fine details. A two-phase training strategy is applied whereby in the first phase the encoder is initially frozen while training the decoder on the public SemiSeg dataset, then in the second phase, the encoder is partially unfrozen while the whole model is trained on the publicly available MedSeg dataset. The model achieves a ten-run mean sensitivity value of 0.790 ± 0.012, an average Dice Similarity Coefficient (DSC) score of 0.781 ± 0.0068, and an average specificity of 0.962 ± 0.0049, outperforming the sensitivity results obtained by recent models such as NextSeg of 2024 and GFNet of 2022 by 8.07% and 7.48%, respectively. These findings demonstrate the potential of CoviSwin as an effective model for clinical COVID-19 lesion segmentation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".