Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

Yufei Cui; Yixin Chen; Zhifeng Shuai; Fang Peng; Yanbo Lv; Luoning Zheng; Xue Liu; Antoni B. Chan; Tei-Wei Kuo; Chun Jason Xue

Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

Yufei Cui, Yixin Chen, Zhifeng Shuai^*, Fang Peng^*, Yanbo Lv, Luoning Zheng, Xue Liu, Antoni B. Chan, Tei-Wei Kuo, Chun Jason Xue

^*Corresponding author for this work

Department of Computer Science

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

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/.

Original language	English
Number of pages	4
Publication status	Published - Nov 2022
Event	36th Conference on Neural Information Processing Systems (NeurIPS 2022) - Hybrid, New Orleans Convention Center, New Orleans, United States Duration: 28 Nov 2022 → 9 Dec 2022 https://neurips.cc/ https://nips.cc/Conferences/2022 https://proceedings.neurips.cc/paper_files/paper/2022

Conference

Conference	36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Abbreviated title	NIPS '22
Place	United States
City	New Orleans
Period	28/11/22 → 9/12/22
Internet address	https://neurips.cc/ https://nips.cc/Conferences/2022 https://proceedings.neurips.cc/paper_files/paper/2022

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@conference{2479886f4ce447729ead4deb19da1f7d,

title = "Precise Augmentation and Counting of Helicobacter Pylori in Histology Image",

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/.",

author = "Yufei Cui and Yixin Chen and Zhifeng Shuai and Fang Peng and Yanbo Lv and Luoning Zheng and Xue Liu and Chan, {Antoni B.} and Tei-Wei Kuo and Xue, {Chun Jason}",

year = "2022",

month = nov,

language = "English",

note = "36th Conference on Neural Information Processing Systems (NeurIPS 2022), NIPS '22 ; Conference date: 28-11-2022 Through 09-12-2022",

url = "https://neurips.cc/, https://nips.cc/Conferences/2022, https://proceedings.neurips.cc/paper_files/paper/2022",

}

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 Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review

TY - CONF

T1 - Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

AU - Cui, Yufei

AU - Chen, Yixin

AU - Shuai, Zhifeng

AU - Peng, Fang

AU - Lv, Yanbo

AU - Zheng, Luoning

AU - Liu, Xue

AU - Chan, Antoni B.

AU - Kuo, Tei-Wei

AU - Xue, Chun Jason

PY - 2022/11

Y1 - 2022/11

N2 - 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/.

AB - 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/.

M3 - RGC 32 - Refereed conference paper (without host publication)

T2 - 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Y2 - 28 November 2022 through 9 December 2022

ER -

Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

Abstract

Conference

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