Coarse-to-fine texture analysis for inner cell mass identification in human blastocyst microscopic images
Bibliographic record
Abstract
Accurate identification of different components of a developing human embryo play crucial roles in assessing the quality of such embryo. One of the most important components of a day-5 human embryo is Inner Cell Mass (ICM). ICM is a part of an embryo that will eventually develop into a fetus. In this paper, an automatic coarse-to-fine texture based approach presented to identify regions of an embryo corresponding to the ICM. First, blastocyst area corresponding to the textured regions is recognized using Gabor and DCT features. Next, two ICM localization approaches are introduced to identify a rough estimate of the ICM location. Finally, the boundaries of the ICM region is finalized using a region based level-set. Experimental results on a data set of 220 day-5 human embryo images confirm that the proposed method is capable of identifying ICM with average Precision, Recall, and Jaccard Index of 78.7%, 86.8%, and 70.3%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".