iScout: an intelligent scout for navigating large image sets
Bibliographic record
Abstract
A new intelligent software tool for PACS systems called 'iScout' has been developed that constructs and displays an overview of large series or studies before downloading the set of images to a PACS workstation. The overview consists of two orthogonal cross-section images that allow the user to select and download a subset of images, avoiding long delays that can occur while downloading hundreds or even thousands of images. The iScout also provides a navigational tool, allowing the user to click on anatomical regions and view the relevant slices, while displaying the anatomical location of the image currently being displayed by the PACS workstation software. The construction of an iScout can be done on either a workstation or a server with only minimal overhead that does not significantly affect the speed of loading. A working iScout tool has been integrated with multi-modality PACS workstation software (McKesson Medical Imaging Solutions), and it was found that the iScout can be generated on the workstation with a maximum added overhead of only 3.4 seconds while downloading a study containing 433 512x512 CT images. The iScout is flexible and can generate scouts for virtually all types of CT and MR images, as well as 3D Ultrasound.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".