Summary of Dafoes: Mixing Datasets Towards the Generalization Of Vision-state Deep-learning Force Estimation in Minimally Invasive Robotic Surgery, by Mikel De Iturrate Reyzabal et al.
DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery
by Mikel De Iturrate Reyzabal, Mingcong Chen, Wei Huang, Sebastien Ourselin, Hongbin Liu
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 Medium Difficulty Summary: The paper presents a novel approach to predict contact forces during minimally invasive robotic surgery (MIRS) using deep neural networks. Currently, predicting sensorless force trends requires large and variable datasets, which are not readily available. To address this challenge, the authors introduce a new vision-haptic dataset (DaFoEs) with variable soft environments for training deep neural models. They also propose a pipeline to generalize different vision and state data inputs using a previously validated dataset with different setups. The authors design a variable encoder-decoder architecture to predict forces done by laparoscopic tools using single input or sequence of inputs, leveraging recurrent decoders (R) and temporal sampling to represent tool acceleration. The results demonstrate that mixing datasets improves translation across new domains, achieving a mean relative estimated force error of 5% and 12% for recurrent and non-recurrent models, respectively. Additionally, the authors show that increasing available data by 150% marginally increases transformer effectiveness up to ~15%. Overall, this approach offers a promising solution towards generalizing vision-state force estimation in MIRS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper tries to figure out how to accurately measure forces during robotic surgery. Right now, it’s hard to predict these forces without lots of data and special equipment. To solve this problem, the researchers created a new dataset with different scenarios that can help train computers to make predictions. They also developed a way to use information from different sources to improve accuracy. The results show that combining different datasets helps make better predictions, which is important for robotic surgery. This approach could lead to more accurate measurements and improved surgical outcomes. |
Keywords
» Artificial intelligence » Encoder decoder » Transformer » Translation
