Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy
Bibliographic record
Abstract
To overcome the half a wavelength resolution limitations of ultrasound imaging, microbubbles (MBs) have been utilized widely in the field. Conventional MB localization methods are limited whether by exhaustive parameter tuning or considering a fixed Point Spread Function (PSF) for MBs. This questions their adaptability to different imaging settings or depths. As a result, development of methods that don’t rely on manually adjusted parameters is crucial. Previously, we used a transformer-based approach i.e. DEtection TRansformer (DETR) [1], [2] to address the above mentioned issues. However, DETR suffers from long training times and lower precision for smaller objects. In this paper, we propose the application of DEformable DETR (DE-DETR) [3] for MB localization to mitigate DETR’s above mentioned challenges. As opposed to DETR, where attention is casted upon all grid pixels, DE-DETR utilizes a multi-scale deformable attention to distribute attention within a limited budget. To evaluate the proposed strategy, pre-trained DE-DETR was fine-tuned on a subset of the dataset provided by the IEEE IUS Ultra-SR challenge organizers using transfer learning principles and subsequently we tested the network on the rest of the dataset, excluding the highly correlated frames. The results manifest an improvement both in precision and recall and the final super-resolution maps compared to DETR.
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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 | 0.001 |
| 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".