Transfer Learning Approaches for Neonate Head Localization from Pressure Images
Bibliographic record
Abstract
This paper explores the use of two types of transfer learning for the task of neonatal head localization from pressure images: 1) The pretrained CNN portion of the PressureNet model, a deep learning model that estimates adult pose given a pressure image, is used for transfer learning for a neonatal population. 2) Annotation of the training patient head locations was completed in the RGB image domain, then transferred to the pressure image domain of application. A multi-modal neonatal patient dataset suitable for this task was used. Data was simultaneously collected from a RGB-D video camera placed above the patient and a pressure sensitive mat (PSM) beneath the patient. Geometric transforms were used to achieve spatial registration between the video image plane and the PSM plane. Patient localization is important in the application of noncontact monitoring for vital sign estimation and movement detection. In testing on unseen patients, 54% of detections made by the object detection model achieved an IoU of 0.5 or greater. This is higher than the accuracy (33%) achieved using a pre-trained ResNet model trained with pressure images converted to RGB. This study demonstrates the potential for cross-domain transfer learning between RGB image and PSM domains.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".