TongueTransUNet: Toward Effective Tongue Contour Segmentation Using Small Dataset
Bibliographic record
Abstract
Abstract Medical image segmentation is important for extracting desired objects among complex human structures to enable further analysis. In the case of lingual ultrasound, it is important to extract tongue contour to understand the language behaviour, which enables lingual ultrasound to act as a biofeedback. In order to segment tongue from ultrasound images, we need to train the deep-learning model on a large dataset, which made it challenging to generalize it using a wide variety of images as it is difficult to collect this huge data. In this research, we are proposing a strategy and generalized model that can work effectively using a well-managed small dataset. This article presents a hybrid architecture using UNet, Vision Transformer (ViT) and Contrastive loss to build a foundation model cumulatively. The process starts with building a reference representation in the embedding space using human experts to validate any new input for training data. UNet and ViT encoders are used to extract the input feature representations. The contrastive loss was then used to compare the new feature embedding with the reference in the embedding space. The UNet-based decoder is used to reconstruct the image to its original size. Before releasing the final results, a quality control process is used to assess the value of the segmented contour, and if rejected, the algorithm requests an action from a human expert to annotate it manually. The results show an improved accuracy over the traditional techniques and can be generalized as it contains only high-quality and relevant features related to the tongue in the embedding space.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".