Infrared Imaging Tools for Necrotizing Enterocolitis (NEC) Diagnosis Guided by RGB-D Sensing
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Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is a disease that leads to inflammation in the intestinal tissue of premature babies. In this paper, we present a novel automated image acquisition and processing system that integrates infrared and RGB-D sensors for NEC detection. Intersensor calibration and data registration are introduced to ensure the consistency of depth, color and infrared images captured by the multispectral sensor. Segmentation of a baby’s torso area is automatically achieved over the infrared imagery while relying on depth and color data to entirely retrieve the region of interest. Analysis of thermal distribution over the whole area reduces the risk of missing key information due to manual intervention. Preliminary results obtained with this multispectral imaging approach for NEC diagnosis are encouraging.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it