Summary of Surgical-dino: Adapter Learning Of Foundation Models For Depth Estimation in Endoscopic Surgery, by Beilei Cui et al.
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
by Beilei Cui, Mobarakol Islam, Long Bai, Hongliang Ren
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to depth estimation in robotic surgery, building upon the foundation model DINOv2. The authors design a low-rank adaptation (LoRA) of DINOv2 for surgical depth estimation, which they refer to as Surgical-DINO. This method incorporates LoRA layers and integrates them into DINO to adapt to domain-specific knowledge in endoscopic surgery. By freezing the DINO image encoder and only optimizing the LoRA layers and depth decoder, the authors demonstrate that their model significantly outperforms state-of-the-art models on a MICCAI challenge dataset of SCARED, collected from da Vinci Xi endoscope surgery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about using artificial intelligence to help robots perform surgeries more accurately. The researchers took a powerful AI model and adapted it for use in surgical environments. They tested their approach and found that it worked much better than other methods on a dataset of real surgery recordings. This could lead to improved accuracy and safety in robotic surgeries. |
Keywords
» Artificial intelligence » Decoder » Depth estimation » Encoder » Lora » Low rank adaptation
