Stem-Cell Localization: A Deconvolution Problem
Bibliographic record
Abstract
Hematopoietic Stem Cells (HSCs) form blood and immune cells and are responsible for the constant renewal of blood. To produce new blood cells, HSCs proliferate and differentiate to different blood cell types continuously during their lifetime. Hence they are of substantial interest in stem cell therapy and cancer research. To classify HSCs to different groups, they must be observed/tracked over time and their key features including cell size, shape, and motility must be extracted. The manual tracking is an onerous task and automated methods are in high demand. The first stage of an semi-automatic/automatic tracking system is cell segmentation. In our previous work we addressed the cell segmentation/localization problem. Modelling adjacent or splitting cells is very challenging and our previous methods might fail to accurately model a group of adjacent cells or a splitting cell. In this paper we address this issue and propose a deconvolution method to precisely model individual HSCs as well as adjacent (splitting) HSCs. An optimization algorithm is combined with a template matching method to segment cell regions and locate the cell centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".