Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge

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

doi:10.1109/BIBM55620.2022.9995101

Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge

Yi Lin
, Jingchi Jiang
, Dongxin Chen
, Zhaoyang Ma
, 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

12 Scopus citations

Abstract

Acne Vulgaris seriously affects people's daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.

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	1639-1643
Number of pages	5
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995101
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
convolutional neural networks
medical image processing
skin disease

Access to Document

10.1109/BIBM55620.2022.9995101

Cite this

Lin, Y., Jiang, J., Chen, D., Ma, Z., Guan, Y., Liu, X., You, H., Yang, J., & Cheng, X. (2022). Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge. 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. 1639-1643). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995101

Lin, Yi ; Jiang, Jingchi ; Chen, Dongxin et al. / Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge. 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. 1639-1643 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{df8917355eb0410e931bd3aa95981531,

title = "Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge",

abstract = "Acne Vulgaris seriously affects people's daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.",

keywords = "acne vulgaris, convolutional neural networks, medical image processing, skin disease",

author = "Yi Lin and Jingchi Jiang and Dongxin Chen and Zhaoyang Ma 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/BIBM55620.2022.9995101",

language = "英语",

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

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

pages = "1639--1643",

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, Chen, D, Ma, Z, Guan, Y, Liu, X, You, H, Yang, J & Cheng, X 2022, Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge. 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. 1639-1643, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995101

Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge. / Lin, Yi; Jiang, Jingchi; Chen, Dongxin 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. 1639-1643 (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 - Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge

AU - Lin, Yi

AU - Jiang, Jingchi

AU - Chen, Dongxin

AU - Ma, Zhaoyang

AU - Guan, Yi

AU - Liu, Xiguang

AU - You, Haiyan

AU - Yang, Jing

AU - Cheng, Xue

PY - 2022

Y1 - 2022

N2 - Acne Vulgaris seriously affects people's daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.

AB - Acne Vulgaris seriously affects people's daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.

KW - acne vulgaris

KW - convolutional neural networks

KW - medical image processing

KW - skin disease

UR - https://www.scopus.com/pages/publications/85146695628

U2 - 10.1109/BIBM55620.2022.9995101

DO - 10.1109/BIBM55620.2022.9995101

M3 - 会议稿件

AN - SCOPUS:85146695628

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

SP - 1639

EP - 1643

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.

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

Y2 - 6 December 2022 through 8 December 2022

ER -

Lin Y, Jiang J, Chen D, Ma Z, Guan Y, Liu X et al. Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge. 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. 1639-1643. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995101

Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge

Abstract

Publication series

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UN SDGs

Keywords

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