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Conference Paper
Tracker Learning Surgical Images By Self-Supervised Learning: An Enhanced Unsupervised Deep Tracking Approach
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- Authors
- Yong Eun Jang, In-su Jang, Kwang-Ju Kim
- Issue Date
- 2023-08
- Citation
- International Conference on Platform Technology and Service (PlatCon) 2023, pp.114-117
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Tracking the surgical site in surgical images is a method that can help doctors. Therefore, we proposed a tracker model that learns by self-supervised learning for tissue organization in surgical images. We constructed a model based on unsupervised deep tracking (UDT) as a base line Our proposed model incorporates forward and backward prediction in conjunction with a Siamese network, a feature that sets it apart as a robust tracking solution. Through a series of extensive experiments, we established the effectiveness of our proposed model in comparison with existing models in the field. Our model achieved an Expected Average Overlap (EAO) score of 0.405, demonstrating significant improvements over existing models: TransT (0.274), KIT (0.223), SRV (0.293), and MEDCVR (0.302), with respective score improvements of 0.131, 0.182, 0.112, and 0.103. These results confirm that our proposed model can be helpful in performing effective tracking in surgical videos.
- KSP Keywords
- Base line, Forward and Backward, Proposed model, Robust tracking, Siamese network, model-based, self-supervised learning, tracking solution