Molecular Formula Image Segmentation with Shape Constraint Loss and Data Augmentation

Ruiqi Jia, Wenjie Xie,Baole Wei,Guanren Qiao, Yang Zhou, Xianjun Lyu,Zhi Tang

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2022)

引用 0|浏览0
暂无评分
摘要
The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.
更多
查看译文
关键词
segmentation,shape constraint loss,molecular
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要