Deep Learning for Automatic Tracking of Tongue Surface in Real-time\n Ultrasound Videos, Landmarks instead of Contours
Bibliographic record
Abstract
One usage of medical ultrasound imaging is to visualize and characterize\nhuman tongue shape and motion during a real-time speech to study healthy or\nimpaired speech production. Due to the low-contrast characteristic and noisy\nnature of ultrasound images, it might require expertise for non-expert users to\nrecognize tongue gestures in applications such as visual training of a second\nlanguage. Moreover, quantitative analysis of tongue motion needs the tongue\ndorsum contour to be extracted, tracked, and visualized. Manual tongue contour\nextraction is a cumbersome, subjective, and error-prone task. Furthermore, it\nis not a feasible solution for real-time applications. The growth of deep\nlearning has been vigorously exploited in various computer vision tasks,\nincluding ultrasound tongue contour tracking. In the current methods, the\nprocess of tongue contour extraction comprises two steps of image segmentation\nand post-processing. This paper presents a new novel approach of automatic and\nreal-time tongue contour tracking using deep neural networks. In the proposed\nmethod, instead of the two-step procedure, landmarks of the tongue surface are\ntracked. This novel idea enables researchers in this filed to benefits from\navailable previously annotated databases to achieve high accuracy results. Our\nexperiment disclosed the outstanding performances of the proposed technique in\nterms of generalization, performance, and accuracy.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".