Extending case-based reasoning by discovering and using image features in IVF
Bibliographic record
Abstract
This paper describes the application of automated image analysis to evaluate morphology and developmental features of oocytes and embryos in the domain of in-vitro fertilization (IVF). Although humans can analyze images more flexibly, computer vision techniques make the process more objective and precise. We propose to use computer-based morphometry to precisely and objectively identify developmental features of oocytes and embryos. Extracted morphological information can be linked with symbolic information to better predict pregnancy outcome and suggest further medical procedures. Recognized features can then be used to support case-based reasoning and knowledge discovery. The combination of image analysis techniques and case-based reasoning can thus serve as: (1) a feature extraction technique; (2) an indexing approach; and (3) an analysis tool. A combination of symbolic and image information can then be used to identify morphological features of oocytes and embryos that are vital for successful IVF. Extracting image features and analyzing them helps to perform knowledge discovery from images.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".