Automated detection of grayscale bar and distance scale in ultrasound images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer assisted diagnosis algorithms are evaluated by testing them against wide-ranging sets of images arising from real clinical conditions. Detection of the distance scale and the reference grayscale present in most ultrasound images can be used to automate the calibration of physical per-pixel distances and grayscale normalization over heterogeneously acquired ultrasound datasets. This work presents novel methods for automated detection of (i) the distance scale and the spacing between its gradations, (ii) the reference grayscale. The distance scale was detected by searching for regular peaks in the 1-D autocorrelation of image pixel columns. The grayscale bar was detected by searching for contiguous sets of columns with long sequences of monotonically changing intensity. In tests on over 1000 images the distance scale detection rate was 94.8% and the correct gradation spacing was determined 91.2% of the time. The reference grayscale detection rate was 100%. A confidence measure was also introduced to characterize the certainty of the distance scale detection. An optimal confidence threshold for flagging low-confidence results that minimizes human intervention without risk of incorrect results remaining unflagged was established through ROC curve analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 it