# The Role of Pleura and Adipose in Lung Ultrasound AI

arXiv: https://arxiv.org/abs/2201.07368 · HTML: https://mlsanity.com/paper/2201.07368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 19 Jan 2022.

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

- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)](https://mlsanity.com/paper/2004.12084/) (2020)
- [Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis](https://mlsanity.com/paper/2009.06116/) (2020)

## Built on by (most cited first, 2 of 2)

- [Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks](https://mlsanity.com/paper/2206.08398/) (2022)
- [Improving Model's Interpretability and Reliability using Biomarkers](https://mlsanity.com/paper/2402.12394/) (2024)

## Abstract (arXiv)

In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin's space artifacts such as A-lines and B-lines) improved the AI model's diagnostic accuracy.