Transformer Lesion Tracker

نویسندگان

چکیده

Evaluating lesion progression and treatment response via longitudinal tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs time consumption when matching is done manually. Previous methods typically lack the integration of local global information. In work, we propose transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, design Cross Attention-based (CAT) to capture combine both information enhance feature extraction. We also develop Registration-based Anatomical Attention Module (RAAM) introduce anatomical CAT so that it can focus on useful knowledge. A Sparse Selection Strategy (SSS) presented selecting features reducing memory footprint training. addition, use regression further improve model performance. conduct experiments public dataset show superiority our method find performance has improved average Euclidean center error at least 14.3% (6mm vs. 7mm) compared with state-of-the-art (SOTA). Code available https://github.com/TangWen920812/TLT.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-16446-0_19