An Unsupervised Learning Scheme for DNA Microarray Image Spot Detection
Bibliographic record
Abstract
DNA microarrays are novel and powerful techniques, which are used to analyze the expression level of DNA, and have many applications in pharmacology, medical diagnosis, environmental engineering, and biological sciences. The process of separating the background from the foreground is a crucial stage in DNA microarray data analysis, since it substantially affects the subsequent stages. Quite a few image processing techniques have been proposed in this direction, including circlebased methods, seeded region growing, histogram-based segmentation, and clustering-based techniques. Of these, the latter method is an emerging topic in microarray image segmentation. We propose an optimized clustering-based microarray image segmentation approach that includes a noise-removal stage. The experiments show that our method performs microarray image segmentation more accurately than the previous clustering-based microarray image segmentation methods, and capture a larger number of true foreground pixels than the seeded region growing method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".