Home Community Revolutionizing Robotic Surgery with Neural Networks: Overcoming Catastrophic Forgetting through Privacy-Preserving Continual Learning in Semantic Segmentation

Revolutionizing Robotic Surgery with Neural Networks: Overcoming Catastrophic Forgetting through Privacy-Preserving Continual Learning in Semantic Segmentation

Revolutionizing Robotic Surgery with Neural Networks: Overcoming Catastrophic Forgetting through Privacy-Preserving Continual Learning in Semantic Segmentation

Deep Neural Networks (DNNs) excel in enhancing surgical precision through semantic segmentation and accurately identifying robotic instruments and tissues. Nevertheless, they face catastrophic forgetting and a rapid decline in performance on previous tasks when learning latest ones, posing challenges in scenarios with limited data. DNNs’ struggle with catastrophic forgetting hampers their proficiency in recognizing previously learned instruments or anatomical structures, especially when updated data is introduced, or old data is inaccessible because of privacy concerns. This limitation underscores the necessity for modern solutions to make sure continual learning and data management in robot-assisted surgery.

Continual learning methods could be exemplar-based, counting on old task samples, or exemplar-free, not requiring old exemplars. Nevertheless, existing approaches mainly deal with classification tasks, posing challenges for semantic segmentation because of background shift issues. In image synthesis, techniques like GAN-based synthesis and image mixing/compositing are used, but they often require large data collections or simulator-based datasets. These methods might not be suitable for complex segmentation tasks and could be resource-intensive.

A recent IEEE Transactions on Medical Imaging paper addresses the restrictions of DNNs in robot-assisted surgery and presents a promising solution. This privacy-preserving synthetic continual semantic segmentation framework combines open-source old instrument foregrounds with synthesized backgrounds and integrates latest instrument foregrounds with extensively augmented real backgrounds. Furthermore, the framework introduces modern techniques corresponding to overlapping class-aware temperature normalization (CAT) and multi-scale shifted-feature distillation (SD) to reinforce model learning utility significantly.

The proposed methodology introduces several modern approaches to handle the challenges of continual learning in semantic segmentation, particularly in robotic surgery. It presents a privacy-preserving synthetic data generation method using StyleGAN-XL, ensuring realistic background tissue images without compromising patient privacy. This approach is a departure from relying solely on real patient data, a typical practice in the sector. As well as, the methodology incorporates mixing and harmonization techniques to reinforce the realism of synthetic images, mitigating variations in environmental aspects, that are crucial for model robustness in surgical scenarios. The authors also introduced CAT, which allows for controlling learning utility for various classes, addressing the imbalance between old and latest classes without catastrophic forgetting. Fourthly, the strategy employs multi-scale shifted-feature distillation to retain spatial relationships amongst semantic objects, overcoming the restrictions of conventional feature distillation methods. Moreover, the synthetic CAT-SD approach combines pseudo-rehearsal with synthetic images, extending the applicability of rehearsal strategies to complex datasets without privacy concerns. Finally, by combining multiple distillation losses, including each logits and have distillation, the methodology achieves a balance between model rigidity and suppleness, ensuring effective continual learning without compromising performance. These innovations collectively position the proposed methodology as a comprehensive solution tailored to the unique demands of semantic segmentation in robotic surgery, offering significant advancements over existing approaches.

The experiments evaluated the proposed method using EndoVis 2017 and 2018 datasets. Results demonstrated the strategy’s effectiveness in mitigating catastrophic forgetting and achieving balanced performance across old and latest instrument classes. Moreover, robustness testing showed superior performance under various uncertainties in comparison with baseline methods. An ablation study was conducted to investigate the effect of hyperparameters on the proposed approach and the synthetic continual learning with CAT-SD method. It investigated the impact of temperature and scaling parameters on model performance, revealing optimal settings that significantly improved learning outcomes, especially in preserving knowledge of old classes while learning latest ones. Moreover, the study underscored the importance of synthetic data generation and continual learning techniques in bolstering model robustness and stopping catastrophic forgetting. The experiments validated the proposed method’s efficacy in privacy-preserving continual learning for semantic segmentation in robotic surgery.

In conclusion, this study introduces a novel privacy-preserving synthetic continual semantic segmentation approach for robotic instrument segmentation. The developed CAT-SD scheme effectively mitigates catastrophic forgetting, addresses data scarcity, and ensures privacy in medical datasets. Extensive experiments reveal superior performance in comparison with state-of-the-art techniques, striking a balance between rigidity and plasticity. Future work will explore incremental domain adaptation techniques to reinforce model adaptability further.

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Mahmoud is a PhD researcher in machine learning. He also holds a
bachelor’s degree in physical science and a master’s degree in
telecommunications and networking systems. His current areas of
research concern computer vision, stock market prediction and deep
learning. He produced several scientific articles about person re-
identification and the study of the robustness and stability of deep

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