Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, automated LUS analysis remains challenging because of speckle noise, imaging artifacts, and operator-dependent acquisition variability. In this work, we present a deep learning framework for multi-class LUS video classification that explores two components: hierarchy-aware training, and anatomy-guided learning. Starting from a strong baseline, we introduce hierarchical training strategies and then introduce pleural line mask supervision to guide model attention toward anatomically relevant regions. We study four clinically relevant classes--h...
| Authors | Alya Almsouti, Lotfi Mecharbat, Noha Aboukhater et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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