# Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection

arXiv: https://arxiv.org/abs/2201.10166 · HTML: https://mlsanity.com/paper/2201.10166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers](https://mlsanity.com/paper/1801.05173/) (2018)
- [POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)](https://mlsanity.com/paper/2004.12084/) (2020)
- [Building medical image classifiers with very limited data using segmentation networks](https://mlsanity.com/paper/1808.05205/) (2018)
- [Fine tuning U-Net for ultrasound image segmentation: which layers?](https://mlsanity.com/paper/2002.08438/) (2020)
- [Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?](https://mlsanity.com/paper/1909.04130/) (2019)

## Built on by (most cited first, 6 of 6)

- [Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks](https://mlsanity.com/paper/2206.08398/) (2022)
- [Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification](https://mlsanity.com/paper/2310.13279/) (2023)
- [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)
- [Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies](https://mlsanity.com/paper/2411.05029/) (2024)
- [Osteoporosis Prediction from Hand X-ray Images Using Segmentation-for-Classification and Self-Supervised Learning](https://mlsanity.com/paper/2412.05345/) (2024)

## Abstract (arXiv)

We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We compare the performance of U-Net trained with dense and sparse labels to segment A-lines, B-lines, and Pleural lines on a custom dataset of lung ultrasound scans from 4 patients. Our experiments show that dense labels help reduce false positive detection. We study the classification capability of the dense and sparse trained U-Net and contrast it with a non-pretrained U-Net, to detect and differentiate COVID-19 and Pneumonia on a large ultrasound dataset of about 40k curvilinear and linear probe images. Our segmentation-based models perform better classification when using pretrained segmentation weights, with the dense-label pretrained U-Net performing the best.