# Ensemble Learning for Microbubble Localization in Super-Resolution Ultrasound

arXiv: https://arxiv.org/abs/2411.07376 · HTML: https://mlsanity.com/paper/2411.07376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Weighted boxes fusion: Ensembling boxes from different object detection models](https://mlsanity.com/paper/1910.13302/) (2019)
- [A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy](https://mlsanity.com/paper/2310.08143/) (2023)
- [Transformer-Based Microbubble Localization](https://mlsanity.com/paper/2209.11859/) (2022)
- [Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy](https://mlsanity.com/paper/2308.09845/) (2023)
- [Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy](https://mlsanity.com/paper/2209.10057/) (2022)

## Abstract (arXiv)

Super-resolution ultrasound (SR-US) is a powerful imaging technique for capturing microvasculature and blood flow at high spatial resolution. However, accurate microbubble (MB) localization remains a key challenge, as errors in localization can propagate through subsequent stages of the super-resolution process, affecting overall performance. In this paper, we explore the potential of ensemble learning techniques to enhance MB localization by increasing detection sensitivity and reducing false positives. Our study evaluates the effectiveness of ensemble methods on both in vivo and simulated outputs of a Deformable DEtection TRansformer (Deformable DETR) network. As a result of our study, we are able to demonstrate the advantages of these ensemble approaches by showing improved precision and recall in MB detection and offering insights into their application in SR-US.