ARSeg: An Attention RegSeg Architecture for CXR Lung Segmentation
Bibliographic record
Abstract
Lung disease is one of the most prevalent causes of illness and death in adults and children worldwide. Researchers focused in developing computer-aided diagnosis/detection systems to provide a visual proof and to detect subtle signs of lung diseases. Various systems were proposed to separate the lung regions from organs and other tissues on Chest X-Ray images. However, there is still challenging cases such as the presence of the rib cage edges and lungs affected with abnormalities. For such, we present a new developed segmentation method called Attention RegSeg to detect the precise lung regions and improve the performance of the lung segmentation task. Attention RegSeg employs attention mechanism to suppress irrelevant features transmitted by skip connections. Experimental results showed an interesting performance with an accuracy of 95.69% and an F1-score of 95.63% using varying loss functions and three datasets (Montgomery, Shenzhen, and JSRT) as learning data. The proposed model outperformed recent state-of-the-art works and proved its ability to segment lungs overcoming challenging cases such as lungs affected with tuberculosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".