Transformer-Based Microbubble Localization
Bibliographic record
Abstract
Ultrasound Localization Microscopy (ULM) is an emerging technique that employs the localization of echogenic microbubbles (MBs) to finely sample and image the microcirculation beyond the diffraction limit of ultrasound imaging. Conventional MB localization methods are mainly based on considering a specific Point Spread Function (PSF) for MBs, which leads to loss of information caused by overlapping MBs, non-stationary PSFs, and harmonic MB echoes. Therefore, it is imperative to devise methods that can accurately localize MBs while being resilient to MB nonlinearities, and variations of MB concentrations that distort MB PSFs. This paper proposes a transformer-based MB localization approach to address this issue. We adopted DEtection TRansformer (DETR) [1], which is an end-to-end object recognition method that detects a unique bounding box for each of the detected objects using set-based Hungarian loss and bipartite matching. To the authors' knowledge, this is the first time transformers have been used for MB localization. To appraise the proposed strategy, the pre-trained DETR network's performance has been tested for detecting MBs using transfer learning principles. We have fine-tuned the network on a subset of randomly selected frames of the dataset provided by the IEEE IUS Ultra-SR challenge organizers and then tested on the rest using cross-validation. For the simulation dataset, the paper supports the deployment of transformer-based solutions for MB localization at high accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".