Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading

Zeming Zhang; Zhuoran Liu; Jingchi Jiang; Chaoran Kong; Yi Guan; Xiguang Liu; Haiyan You; Jing Yang; Yi Lin

doi:10.1109/BIBM58861.2023.10385781

Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading

Zeming Zhang
, Zhuoran Liu
, Jingchi Jiang
, Chaoran Kong
, Yi Guan^*
, Xiguang Liu^*
, Haiyan You
, Jing Yang
, Yi Lin^*

^*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

1 Scopus citations

Abstract

Acne seriously affects people's daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3414-3421
Number of pages	8
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385781
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	5/12/23 → 8/12/23

Keywords

acne severity grading
deep learning
interpretable diagnosis
lesion localization

Access to Document

10.1109/BIBM58861.2023.10385781

Cite this

Zhang, Z., Liu, Z., Jiang, J., Kong, C., Guan, Y., Liu, X., You, H., Yang, J., & Lin, Y. (2023). Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 3414-3421). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385781

Zhang, Zeming ; Liu, Zhuoran ; Jiang, Jingchi et al. / Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3414-3421 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

@inproceedings{326718ca4aa54d2b9f65a6727f90116b,

title = "Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading",

abstract = "Acne seriously affects people's daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.",

keywords = "acne severity grading, deep learning, interpretable diagnosis, lesion localization",

author = "Zeming Zhang and Zhuoran Liu and Jingchi Jiang and Chaoran Kong and Yi Guan and Xiguang Liu and Haiyan You and Jing Yang and Yi Lin",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/BIBM58861.2023.10385781",

language = "英语",

series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

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

pages = "3414--3421",

editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

address = "美国",

}

Zhang, Z, Liu, Z, Jiang, J, Kong, C, Guan, Y, Liu, X, You, H, Yang, J & Lin, Y 2023, Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3414-3421, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 5/12/23. https://doi.org/10.1109/BIBM58861.2023.10385781

Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading. / Zhang, Zeming; Liu, Zhuoran; Jiang, Jingchi et al.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3414-3421 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

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

TY - GEN

T1 - Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading

AU - Zhang, Zeming

AU - Liu, Zhuoran

AU - Jiang, Jingchi

AU - Kong, Chaoran

AU - Guan, Yi

AU - Liu, Xiguang

AU - You, Haiyan

AU - Yang, Jing

AU - Lin, Yi

PY - 2023

Y1 - 2023

N2 - Acne seriously affects people's daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.

AB - Acne seriously affects people's daily lives. Several studies of automated acne diagnosis either lack reasonable interpretation to support the diagnosis or ignore evidence in the diagnosis process. In this paper, we propose an interpretable diagnosis framework for face acne. This framework uses complementation learning of evidence localization and severity level grading to boost both streams by sharing the features that support each other. Evidence localization learns to identify lesion areas for supporting the diagnosis stream as well as providing interpretation. Severity level grading learns to recognize the diagnosis result and also provides reference and rectification for evidence localization. Experimental results show that complementation learning improves both evidence localization and severity level grading, the lesion areas from evidence localization can support the diagnosis and provide interpretations, and the diagnosis framework reaches the state-of-the-art level and the diagnostic performance of dermatologists.

KW - acne severity grading

KW - deep learning

KW - interpretable diagnosis

KW - lesion localization

UR - https://www.scopus.com/pages/publications/85184874442

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DO - 10.1109/BIBM58861.2023.10385781

M3 - 会议稿件

AN - SCOPUS:85184874442

T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

SP - 3414

EP - 3421

BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

A2 - Hu, Xiaohua

A2 - Engel, Felix

A2 - Mahmud, Mufti

A2 - Pisanti, Nadia

A2 - Cui, Xuefeng

A2 - Song, Hong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Y2 - 5 December 2023 through 8 December 2023

ER -

Zhang Z, Liu Z, Jiang J, Kong C, Guan Y, Liu X et al. Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3414-3421. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385781

Interpretable Diagnosis of Face Acne via Complementation Learning of Evidence Localization and Severity Level Grading

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