CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis

Yi Lin; Jingchi Jiang; Zhaoyang Ma; Dongxin Chen; Yi Guan; Xiguang Liu; Haiyan You; Jing Yang; Xue Cheng

doi:10.1109/BIBM55620.2022.9995406

CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis

Yi Lin
, Jingchi Jiang
, Zhaoyang Ma
, Dongxin Chen
, Yi Guan^*
, Xiguang Liu^*
, Haiyan You
, Jing Yang
, Xue Cheng

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Heilongjiang Provincial Hospital

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

The collection and publication of medical images on the face are quite difficult because of the invasion of privacy. Meanwhile, it takes a major expenditure of time and effort to manually label large-scale face images covered with s o many fine skin lesions. In this work, a multi-class object large-scale image inpainting model Class-Guided PG-GAN (CGPG-GAN) is proposed and its application in boosting downstream model performances is explored. This model is applied on face acne lesion inpainting where the image size is very large and missing areas are different types of lesions. The experiment results show that our method is superior to some existing methods and can improve the performance of downstream diagnosis remarkably.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1634-1638
Number of pages	5
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995406
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: 6 Dec 2022 → 8 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	6/12/22 → 8/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

acne vulgaris
generative adversarial networks
image inpainting
skin disease

Access to Document

10.1109/BIBM55620.2022.9995406

Cite this

Lin, Y., Jiang, J., Ma, Z., Chen, D., Guan, Y., Liu, X., You, H., Yang, J., & Cheng, X. (2022). CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 1634-1638). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995406

Lin, Yi ; Jiang, Jingchi ; Ma, Zhaoyang et al. / CGPG-GAN : An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1634-1638 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{198b614fa30c4cc2b3ddd7d2fbb46523,

title = "CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis",

abstract = "The collection and publication of medical images on the face are quite difficult because of the invasion of privacy. Meanwhile, it takes a major expenditure of time and effort to manually label large-scale face images covered with s o many fine skin lesions. In this work, a multi-class object large-scale image inpainting model Class-Guided PG-GAN (CGPG-GAN) is proposed and its application in boosting downstream model performances is explored. This model is applied on face acne lesion inpainting where the image size is very large and missing areas are different types of lesions. The experiment results show that our method is superior to some existing methods and can improve the performance of downstream diagnosis remarkably.",

keywords = "acne vulgaris, generative adversarial networks, image inpainting, skin disease",

author = "Yi Lin and Jingchi Jiang and Zhaoyang Ma and Dongxin Chen and Yi Guan and Xiguang Liu and Haiyan You and Jing Yang and Xue Cheng",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9995406",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1634--1638",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, \{Ananda M.\} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

address = "美国",

}

Lin, Y, Jiang, J, Ma, Z, Chen, D, Guan, Y, Liu, X, You, H, Yang, J & Cheng, X 2022, CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 1634-1638, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995406

CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis. / Lin, Yi; Jiang, Jingchi; Ma, Zhaoyang et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1634-1638 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CGPG-GAN

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

AU - Lin, Yi

AU - Jiang, Jingchi

AU - Ma, Zhaoyang

AU - Chen, Dongxin

AU - Guan, Yi

AU - Liu, Xiguang

AU - You, Haiyan

AU - Yang, Jing

AU - Cheng, Xue

PY - 2022

Y1 - 2022

N2 - The collection and publication of medical images on the face are quite difficult because of the invasion of privacy. Meanwhile, it takes a major expenditure of time and effort to manually label large-scale face images covered with s o many fine skin lesions. In this work, a multi-class object large-scale image inpainting model Class-Guided PG-GAN (CGPG-GAN) is proposed and its application in boosting downstream model performances is explored. This model is applied on face acne lesion inpainting where the image size is very large and missing areas are different types of lesions. The experiment results show that our method is superior to some existing methods and can improve the performance of downstream diagnosis remarkably.

AB - The collection and publication of medical images on the face are quite difficult because of the invasion of privacy. Meanwhile, it takes a major expenditure of time and effort to manually label large-scale face images covered with s o many fine skin lesions. In this work, a multi-class object large-scale image inpainting model Class-Guided PG-GAN (CGPG-GAN) is proposed and its application in boosting downstream model performances is explored. This model is applied on face acne lesion inpainting where the image size is very large and missing areas are different types of lesions. The experiment results show that our method is superior to some existing methods and can improve the performance of downstream diagnosis remarkably.

KW - acne vulgaris

KW - generative adversarial networks

KW - image inpainting

KW - skin disease

UR - https://www.scopus.com/pages/publications/85146658797

U2 - 10.1109/BIBM55620.2022.9995406

DO - 10.1109/BIBM55620.2022.9995406

M3 - 会议稿件

AN - SCOPUS:85146658797

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 1634

EP - 1638

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 6 December 2022 through 8 December 2022

ER -

Lin Y, Jiang J, Ma Z, Chen D, Guan Y, Liu X et al. CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1634-1638. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995406

CGPG-GAN: An Acne Lesion Inpainting Model for Boosting Downstream Diagnosis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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