# Weakly Semi-Supervised Detection in Lung Ultrasound Videos

arXiv: https://arxiv.org/abs/2308.04463 · HTML: https://mlsanity.com/paper/2308.04463/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Builds on (most cited first, 15 of 15)

- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [Humble Teachers Teach Better Students for Semi-Supervised Object Detection](https://mlsanity.com/paper/2106.10456/) (2021)
- [Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework](https://mlsanity.com/paper/2103.11402/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection](https://mlsanity.com/paper/2103.16368/) (2021)
- [Interpolation-based semi-supervised learning for object detection](https://mlsanity.com/paper/2006.02158/) (2020)
- [Omni-DETR: Omni-Supervised Object Detection with Transformers](https://mlsanity.com/paper/2203.16089/) (2022)
- [Group R-CNN for Weakly Semi-supervised Object Detection with Points](https://mlsanity.com/paper/2205.05920/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching](https://mlsanity.com/paper/2112.09459/) (2021)
- [Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations](https://mlsanity.com/paper/2206.00274/) (2022)
- [ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans](https://mlsanity.com/paper/2207.01842/) (2022)
- [An interpretable object detection based model for the diagnosis of neonatal lung diseases using Ultrasound images](https://mlsanity.com/paper/2105.10081/) (2021)
- [Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes](https://mlsanity.com/paper/2204.00147/) (2022)

## Built on by (most cited first, 5 of 5)

- [How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound](https://mlsanity.com/paper/2310.03608/) (2023)
- [On-the-Fly Point Annotation for Fast Medical Video Labeling](https://mlsanity.com/paper/2404.14344/) (2024)
- [Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports](https://mlsanity.com/paper/2405.14230/) (2024)
- [Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis](https://mlsanity.com/paper/2503.17475/) (2025)
- [ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis](https://mlsanity.com/paper/2610.02263/) (2026)

## Abstract (arXiv)

Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data. We propose a method for improving object detection in medical videos through weak supervision from video-level labels. More concretely, we aggregate individual detection predictions into video-level predictions and extend a teacher-student training strategy to provide additional supervision via a video-level loss. We also introduce improvements to the underlying teacher-student framework, including methods to improve the quality of pseudo-labels based on weak supervision and adaptive schemes to optimize knowledge transfer between the student and teacher networks. We apply this approach to the clinically important task of detecting lung consolidations (seen in respiratory infections such as COVID-19 pneumonia) in medical ultrasound videos. Experiments reveal that our framework improves detection accuracy and robustness compared to baseline semi-supervised models, and improves efficiency in data and annotation usage.