RGB-D scene analysis in the NICU
Bibliographic record
Abstract
Continuity of care is achieved in the neonatal intensive care unit (NICU) through careful documentation of all events of clinical significance, including clinical interventions and routine care events (e.g., feeding, diaper change, weighing, etc.). As a step towards automating this documentation process, we propose a scene recognition algorithm that can automatically identify key features in a single image of the patient environment, paired with a rule-based sentence generator to caption the scene. Color and depth video were obtained from 29 newborn patients from the Children's Hospital of Eastern Ontario (CHEO) using an Intel RealSense SR300 RGB-D camera and manual bedside event annotation. Image processing techniques are implemented to classify two lighting conditions: brightness level and phototherapy. A deep neural network is developed for three image classification tasks: on-going intervention, bed occupancy, and patient coverage. Transfer learning is leveraged in the feature extraction layers, such that weights learned from a generic data-rich task are applied to the clinical domain where data collection is complex and costly. Different depth fusion techniques are implemented and compared among classification tasks, where the depth and color data are fused as an RGB-D image (image fusion) or separately at various layers in the network (network fusion). Promising results were obtained with >84% sensitivity and >73% F1 measure across all context variables despite the large class imbalance. RGBD-based models are shown to outperform RGB models on most tasks. In general, a 4-channel image fusion and network fusion at the 11th layer of the VGG-16 architecture were preferred. Ultimately, achieving complete scene understanding through multimodal computer vision could form the basis for a semi-automated charting system to assist clinical staff.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".