Summary of Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos, by Ryo Fujii and Ryo Hachiuma and Hideo Saito
Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos
by Ryo Fujii, Ryo Hachiuma, Hideo Saito
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel approach to surgical tool detection in minimally invasive surgery videos, leveraging image-level labels rather than instance-level labels. The current methods rely heavily on supervised learning and require large datasets with fully annotated bounding boxes. However, annotating such datasets is time-consuming and burdensome. To address this challenge, the authors introduce a co-occurrence loss that takes into account the fact that some tool pairs often co-occur in an image. This knowledge helps to improve classification performance when tools have similar shapes and textures. The proposed method is evaluated on the Endovis2018 dataset and shows promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding tools in surgery videos. Right now, it’s hard to teach computers to do this because we need lots of pictures with exact labels (like “this tool is here”). But labeling all those images takes a long time! So, the authors came up with a new way to use image-level labels instead. They created something called a co-occurrence loss that helps computers figure out when certain tools are together in an image. This makes it easier for computers to tell apart similar-looking tools. The method was tested on some surgery video data and worked pretty well! |
Keywords
* Artificial intelligence * Classification * Supervised
