SDMA-Net: Swin Transformer-Based Dynamic Memory-Attention Network for Endoscopic Navigation

SDMA-Net: Swin Transformer-Based Dynamic Memory-Attention Network for Endoscopic Navigation

Accurate endoscopic motion navigation is crucial for minimally invasive surgical procedures. Nevertheless, endoscopic video data often exhibit low texture, variable lighting, and dynamic motion patterns, which poses significant challenges to existing methods. To address these issues, we propose a no...

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Bibliographic Details
Main Authors: Runnan Zhang, Qi Tian, Jinghui Chu, Wei Lu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Endoscopic navigation
swin transformer
long short-term memory (LSTM)
classification accuracy
Online Access:https://ieeexplore.ieee.org/document/11025809/
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