Autonomous Ultrasound Scanning Towards Standard Plane Using Interval Interaction Probabilistic Movement Primitives
Bibliographic record
Abstract
Learning from demonstrations is the paradigm where robots acquire new skills demonstrated by an expert and alleviate the physical burden on experts to perform repetitive tasks. Ultrasound scanning is one of the ways to view the anatomical structures of soft tissues, but it is repetitive for some tissue scanning tasks. In this study, an autonomous ultrasound scanning towards a standard plane framework is proposed. Interaction probabilistic movement primitives (iProMP) was proposed for the collaborative tasks for human and robot movement. Inspired by the interval type-2 fuzzy system, an interval iProMP is proposed to learn the ultrasound scanning navigation strategy from scanning demonstrations and the collaborative agents are the robot movement and ultrasound image information. The proposed interval iProMP improves the capacity of dealing with uncertainties due to insufficient observations during reproduction. U-Net is applied to recognize the desired ultrasound image shown during demonstrations and a confidence map is used to evaluate the ultrasound image quality. Breast seroma scanning is chosen as the ultrasound scanning task to validate the performance of the proposed autonomous ultrasound scanning framework. Ultrasound navigation is to realize autonomous ultrasound scanning for localizing the breast seroma. The simulation comparison result shows the better performance of the proposed interval iProMP under insufficient observation, compared to traditional iProMP. The experiment result validates the feasibility and generality of the proposed autonomous ultrasound scanning framework using interval iProMP with a higher success rate than that with traditional iProMP.
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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 | 0.000 |
| 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".