Summary of Egosurgery-phase: a Dataset Of Surgical Phase Recognition From Egocentric Open Surgery Videos, by Ryo Fujii and Masashi Hatano and Hideo Saito and Hiroki Kajita
EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos
by Ryo Fujii, Masashi Hatano, Hideo Saito, Hiroki Kajita
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new egocentric video dataset for open surgery phase recognition, called EgoSurgery-Phase, which consists of 15 hours of real open surgery videos and eye gaze data. The dataset is publicly available and aims to address the lack of datasets for open surgery phase recognition. The authors also introduce a gaze-guided masked autoencoder (GGMAE) model that leverages gaze information to guide the attention process, improving the recognition accuracy compared to previous state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new video dataset and AI model to help doctors recognize different stages of open surgery. The dataset has 15 hours of real surgeries recorded from the doctor’s perspective, along with where their eyes are looking. This information helps the AI focus on important parts of the surgery. The authors also created a special kind of AI called GGMAE that uses this gaze information to get better results. This new model can recognize open surgery stages more accurately than previous models. |
Keywords
» Artificial intelligence » Attention » Autoencoder
