A Data-Centric Approach to Investigate the Feasibility of Utilizing Animal Medical Data as a Solution for Human Medical Data Scarcity
Bibliographic record
Abstract
Reticulocyte count is a routine blood test that can be an essential source of knowledge for medical doctors to diagnose and assess patients’ health condition. In fact, the automation of this blood test will reduce cost and time, in addition to protecting laboratorians’ lives, especially during pandemics and outbreaks. However, human reticulocyte data scarcity is a main challenge that slows the pace of the test automation. In this paper, a novel method that assesses the feasibility of using animal reticulocyte cells as a solution to compensate for the scarcity of human reticulocyte data is investigated. The integration of animal cells will be implemented by utilizing a data-centric artificial intelligence approach, in addition to employing multiple deep classifiers that utilize transfer learning in different experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, to evaluate the effectiveness of the proposed method, three evaluation criteria have been proposed, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures. All the experiments of this work were conducted on a public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score respectively. Moreover, the results showed that using animals medical data holds a promising solution for human medical data scarcity, as utilizing weights that were pretrained on a medium size feline reticulocyte dataset outperformed the model that utilized weights that were pretrained on the large scale ImageNet dataset
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".