Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review

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Author(s)

  • Yixin Chen
  • Zhifeng Shuai
  • Fang Peng
  • Yanbo Lv
  • Luoning Zheng
  • Xue Liu
  • Tei-Wei Kuo

Related Research Unit(s)

Detail(s)

Original languageEnglish
Number of pages4
Publication statusPublished - Nov 2022

Conference

Title36th Conference on Neural Information Processing Systems (NeurIPS 2022)
LocationHybrid, New Orleans Convention Center
PlaceUnited States
CityNew Orleans
Period28 November - 9 December 2022

Abstract

We study the precise counting of Helicobacter Pylori (HP), which is important for diagnosis of gastric cancer. The crowd counting technique is adapted for a precise quantitative analysis. The challenge of training an HP counting model lies in scarcity of labels. We use a DCGAN for the generative modelling of HP morphology and perform high-fidelity data augmentation. The comparative results show our method outperforms the object detection and semantic segmentation baselines. The proposed framework is potential useful in quantitative analysis of other bacteria in histology images. The dataset is available at https://cyxhello. github.io/HPCDataset/.

Citation Format(s)

Precise Augmentation and Counting of Helicobacter Pylori in Histology Image. / Cui, Yufei; Chen, Yixin; Shuai, Zhifeng et al.
2022. Paper presented at 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States.

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review