Bibliographic record
Abstract
Segmentation of ultrasound images is difficult due to the existence of speckle noise. Erroneous edges from speckle noise are not only abundant but also have large magnitude due to the multiplicative nature of speckle noise. Moreover, boundary edges are usually incomplete, being missing or weak at some places. We propose a system to address these problems in two steps. First, based on the observation that boundaries in ultrasound images have the appearance of straight or gently curving line segments, we adopt Sha'ahsua and Ullman's (1988) saliency map method to reduce speckle noise and enhance edges. Then we use a new snake model, which we call a systolic snake, to perform a multi-level feature search. The systolic snake can not only overcome local minima, but also effectively use both strong and weak image information. Furthermore, the system can be used in an automatic system since, unlike other snake models, ours does not need a close initialization The resulting system is tested on some ultrasound loin images and results are promising.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".