Summary of Weakly-supervised Learning Via Multi-lateral Decoder Branching For Tool Segmentation in Robot-assisted Cardiovascular Catheterization, by Olatunji Mumini Omisore et al.
Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization
by Olatunji Mumini Omisore, Toluwanimi Akinyemi, Anh Nguyen, Lei Wang
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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 weakly-supervised learning method for automating tool segmentation in cardiovascular angiogram datasets to improve robot-assisted catheterization. It develops a modified U-Net architecture with multiple lateral decoders that generate diverse pseudo labels under different perturbations, augmenting available partial labels. The model is trained end-to-end and validated using partially annotated data from three procedures, achieving results comparable to fully-supervised models. The paper also compares its performance to existing weakly-supervised learning methods, outperforming them across the datasets. Additionally, the authors conduct ablation studies to demonstrate the model’s consistency under different parameters. Finally, they apply their method for tool segmentation in a robot-assisted catheterization experiment, enhancing visualization with high connectivity indices and a mean processing time of 35 ms per frame. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping doctors use robots to do heart procedures better. They want to make it easier for the doctor and the robot to work together by using artificial intelligence to automatically identify the tools being used during the procedure. The problem is that making the AI model learn from a lot of data takes a long time and costs a lot of money. So, they came up with a new way to train the AI model using less data but still getting good results. They tested their method on real data from three different procedures and it worked well. They also compared it to other similar methods and theirs was better. Finally, they showed that their method can be used in real-life situations with robots to help doctors do heart procedures. |
Keywords
» Artificial intelligence » Supervised
