TranSLUCEnT: Transferred Sequential Lung Ultrasound Characteristic Encodings-based Transformer for Lung Ultrasound Pattern Classification in Premature Neonates

Neonatal respiratory disorders present significant challenges in clinical settings, often demanding rapid and accurate diagnostic solutions for effective patient management. In recent times, lung ultrasound (LUS) has played a notable role in the evaluation of neonatal patients affected by respiratory diseases. However, limited work has been done to automate the process to assist clinicians. To this extent, sufficient representative data is required for reliable training of deep learning (DL)-based methods. To address the challenge of the limited availability of annotated data, this study aims to utilize domain knowledge from an existing adult patient population for effective video-level classification of LUS patterns in newborns. This study introduces TranSLUCEnT, a transformer-based video-level LUS pattern classification model that employs the transfer of domain knowledge from adults to newborns. To this extent, it uses frame-level encodings of LUS data from newborns, extracted from a...