# Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

arXiv: https://arxiv.org/abs/2009.05436 · HTML: https://mlsanity.com/paper/2009.05436/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining](https://mlsanity.com/paper/2210.04477/) (2022)
- [Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning](https://mlsanity.com/paper/2212.04097/) (2022)
- [atTRACTive: Semi-automatic white matter tract segmentation using active learning](https://mlsanity.com/paper/2305.18905/) (2023)
- [Investigate the Essence of Long-Tailed Recognition from a Unified Perspective](https://mlsanity.com/paper/2107.03758/) (2021)
- [Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification](https://mlsanity.com/paper/2303.01707/) (2023)
- [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)

## Abstract (arXiv)

Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative procedure. The core component of TSAL is the multi-label learning mechanism, in which label correlations information is used to design multi-label margin (MLM) strategy and confidence validation for automatically selecting informative samples and confident labels. On this basis, a multi-symptom multi-label (MSML) classification network is proposed to learn discriminative features of lung symptoms, and a human-machine interaction is exploited to confirm the final annotations that are used to fine-tune MSML with progressively labeled data. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6,836 images sampled from 678 videos. Experimental evaluations show that TSAL using only 20% data can achieve superior performance to the baseline and the state-of-the-art. Qualitatively, visualization of both attention map and sample distribution confirms the good consistency with the clinic knowledge.