Summary of Benchmarking Pretrained Attention-based Models For Real-time Recognition in Robot-assisted Esophagectomy, by Ronald L.p.d. De Jong et al.
Benchmarking Pretrained Attention-based Models for Real-Time Recognition in Robot-Assisted Esophagectomy
by Ronald L.P.D. de Jong, Yasmina al Khalil, Tim J.M. Jaspers, Romy C. van Jaarsveld, Gino M. Kuiper, Yiping Li, Richard van Hillegersberg, Jelle P. Ruurda, Marcel Breeuwer, Fons van der Sommen
First submitted to arxiv on: 4 Dec 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 explores the development of a comprehensive dataset for semantic segmentation in robot-assisted minimally invasive esophagectomy (RAMIE), featuring the largest collection of vital anatomical structures and surgical instruments to date. The study aims to understand the challenges and limitations of current state-of-the-art algorithms on this novel dataset and problem, benchmarking eight real-time deep learning models using two pretraining datasets. The findings indicate that pretraining on ADE20k is more effective than pretraining on ImageNet, and attention-based models outperform traditional convolutional neural networks, with SegNeXt and Mask2Former achieving higher Dice scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Esophageal cancer is a major health concern worldwide, traditionally treated using open esophagectomy. However, robot-assisted minimally invasive esophagectomy (RAMIE) has emerged as a promising alternative. This study aims to improve surgical navigation by developing a comprehensive dataset for semantic segmentation in RAMIE, featuring vital anatomical structures and surgical instruments. The research compares different deep learning models on this novel problem, concluding that pretraining on ADE20k is more effective than pretraining on ImageNet, and attention-based models outperform traditional convolutional neural networks. |
Keywords
» Artificial intelligence » Attention » Deep learning » Pretraining » Semantic segmentation
