Segmentation of Patient Images in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Detection and segmentation of people within a scene has been primarily applied to indoor imagery for surveillance systems and outdoor scenes for pedestrian detection. This paper proposes to leverage a similar semantic segmentation model for segmenting patients in the neonatal intensive care unit (NICU) during video-based monitoring. This will serve as part of a noncontact, non-invasive and unobtrusive system to monitor neonates by acquiring a relevant region-of-interest from overhead RGB-D video. This paper examines situations typical of the NICU environment to ensure generalization of the solution to all patient scenarios. Transfer learning is applied to a pre-trained convolutional neural network on three different patients. Promising results are observed when the model is tested on a new patient. Final testing accuracy of 93% demonstrates the potential of such algorithm to automatically determine a suitable region-of-interest for video-based patient monitoring.
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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) | 0.000 | 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 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".