Image Selection Code for Automated Dicentric Chromosome Identification
Bibliographic record
Abstract
Image Selection for Metaphase Images in Automated Dicentric Chromosome Identification Author: Yanxin Li, Jin Liu Correspondence: Peter K. Rogan, Ph.D, progan@uwo.ca This code is for the following paper: Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection Jin Liu, Yanxin Li, Ruth Wilkins, Farrah Flegal, Joan H. Knoll, Peter K. Rogan List of files and folders: DemoSample1: metaphase images for DemoSample1 DemoSample2: metaphase images for DemoSample2 DemoSample1.adcisample: ADCI sample file generated by ADCI software for DemoSample1. DemoSample2.adcisample: ADCI sample file generated by ADCI software for DemoSample2. ADCI sample files contain area and filters information of images which is required by Image Selection code. DemoScript.py: Image Selection code Prerequisites for the Image Selection code: Python 2. Code is tested in Python 2.7.6 NumPy How to run: Run DemoScript.py. Make sure DemoSample1.adcisample and DemoSample2.adcisample are in the same folder as DemoScript.py. Results: For each sample, the code will display image names in descending order of quality, measured by Group Bin Distance or Combined Z Score (using weight 434521). To evaluate the sorting, view metaphase images in DemoSample1 or DemoSample2 folders.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.312 | 0.159 |
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".