Enhancement of Neonatal Lung Pathology Classification Using Multi-view Feature Representation
Bibliographic record
Abstract
In neonates, respiratory related complications are one of the major causes of death. X-rays and chest CT scans are commonly used for diagnosing different lung conditions. Recently Lung Ultrasound (LUS) has gained much attention due to its ionizing-radiation free nature and cost effectiveness. A limitation deterring the wide use of LUS is the lack of specialist clinicians who are trained using LUS. In this paper, a multi-view feature representation system is proposed for automated LUS image classification that could assist diagnosing the lung conditions. Specifically, different features were extracted from the LUS images, generating multi-view features. The feature quality of the Local Binary Patterns (LBP) was improved by using Sparse Coding based Keypoints (SCK) to focus on regions of the image that contain clinically relevant information. After feature extraction, a semantic correlation fusion method, Labeled Multiple Canonical Correlation Analysis (LMCCA), is applied to the multi-view features, leading to a high quality feature representation. The results are then verified with experiments on the Mount Sinai Hospital (MSH) LUS database.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".