# Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks

arXiv: https://arxiv.org/abs/2206.08398 · HTML: https://mlsanity.com/paper/2206.08398/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection](https://mlsanity.com/paper/2201.10166/) (2022)
- [Translational Lung Imaging Analysis Through Disentangled Representations](https://mlsanity.com/paper/2203.01668/) (2022)
- [Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation](https://mlsanity.com/paper/2110.02196/) (2021)
- [The Role of Pleura and Adipose in Lung Ultrasound AI](https://mlsanity.com/paper/2201.07368/) (2022)
- [Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning](https://mlsanity.com/paper/1802.03617/) (2018)

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

- [LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies](https://mlsanity.com/paper/2407.16067/) (2024)
- [Improving Model's Interpretability and Reliability using Biomarkers](https://mlsanity.com/paper/2402.12394/) (2024)
- [LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels](https://mlsanity.com/paper/2411.01144/) (2024)
- [Label Uncertainty for Ultrasound Segmentation](https://mlsanity.com/paper/2508.15635/) (2025)
- [Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis](https://mlsanity.com/paper/2605.18878/) (2026)

## Abstract (arXiv)

Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomarker labels. Notably, biomarker feature extractors can be trained from data annotated with weak video-scale supervision. These features can be used by a variety of downstream Expert models targeted for diverse clinical tasks (Diagnosis, lung severity, S/F ratio). Crucially, task-specific expert models are comparable in accuracy to end-to-end models directly trained for such target tasks, while being significantly lower cost to train.