Summary of Seine: Structure Encoding and Interaction Network For Nuclei Instance Segmentation, by Ye Zhang et al.
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
by Ye Zhang, Linghan Cai, Ziyue Wang, Yongbing Zhang
First submitted to arxiv on: 18 Jan 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 proposes a novel approach to instance segmentation in histopathological images, specifically targeting nuclei instance segmentation. The authors identify two main challenges: similar visual presentation of intranuclear and extranuclear regions causing under-segmentation, and the lack of exploration of nuclei structure resulting in fragmented predictions. To address these issues, they develop a structure encoding and interaction network (SEINE), which incorporates contour-based structure encoding (SE) to model nuclei structure and semantics, as well as structure-guided attention (SGA) modules that enhance structure learning for fuzzy nuclei. Additionally, the authors propose semantic feature fusion (SFF) to boost semantic consistency and position enhancement (PE) to suppress incorrect boundary predictions. The paper demonstrates the superiority of SEINE through extensive experiments on four datasets, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to look at pictures of cells. It’s trying to solve two big problems: making sure the computer can find all the parts of a cell, and making sure those parts are correctly labeled as either inside or outside the cell. To do this, it creates a special kind of map that shows what different parts of the cell look like, and then uses that map to help the computer make better guesses about where the edges of the cell are. It also adds some extra steps to make sure the computer is paying attention to the right things when it’s making its predictions. The authors did lots of tests with this new approach and found that it works really well, even beating other methods that were tried before. |
Keywords
* Artificial intelligence * Attention * Instance segmentation * Semantics
