In our recent work, “Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery”, which was published in IEEE Transactions on Medical Imaging, we propose a state-of-the-art framework for continual semantic segmentation in robotic surgery. This breakthrough addresses catastrophic forgetting in DNNs, enhancing surgical precision without compromising patient privacy.
๐ Privacy-First Synthetic Data: We’ve crafted a solution that blends open-source instrument data with synthesized backgrounds, ensuring real patient data remains confidential.
๐ก Innovative Features:
– Class-Aware Temperature Normalization (CAT) to prevent forgetting of previously learned tasks.
– Multi-Scale Shifted-Feature Distillation (SD) to preserve spatial relationships for robust feature learning.
Check the paper at https://lnkd.in/eTy8KAC5
Code is also available at https://lnkd.in/eMzNs2Be
Co-authors: Mengya Xu, Mobarakol Islam, Long Bai, Hongliang Ren
