Deep attentive feature learning for histopathology image classification

Pengxiang Wu; Hui Qu; Jingru Yi; Qiaoying Huang; Chao Chen; Dimitris Metaxas

doi:10.1109/ISBI.2019.8759267

Deep attentive feature learning for histopathology image classification

Pengxiang Wu
, Hui Qu
, Jingru Yi
, Qiaoying Huang
, Chao Chen
, Dimitris Metaxas

Biomedical Informatics

Rutgers - The State University of New Jersey, New Brunswick

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

15 Scopus citations

Abstract

In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.

Original language	English
Title of host publication	ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
Pages	1865-1868
Number of pages	4
ISBN (Electronic)	9781538636411
DOIs	https://doi.org/10.1109/ISBI.2019.8759267
State	Published - Apr 2019
Event	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: Apr 8 2019 → Apr 11 2019

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2019-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/Territory	Italy
City	Venice
Period	04/8/19 → 04/11/19

Keywords

Attention
Breast
Convolutional neural network
Histopathology image analysis
Transfer learning

Access to Document

10.1109/ISBI.2019.8759267

Cite this

Wu, P., Qu, H., Yi, J., Huang, Q., Chen, C., & Metaxas, D. (2019). Deep attentive feature learning for histopathology image classification. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 1865-1868). Article 8759267 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759267

@inproceedings{c935eee177ef467fa6eaf706ab0bb05e,

title = "Deep attentive feature learning for histopathology image classification",

abstract = "In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.",

keywords = "Attention, Breast, Convolutional neural network, Histopathology image analysis, Transfer learning",

author = "Pengxiang Wu and Hui Qu and Jingru Yi and Qiaoying Huang and Chao Chen and Dimitris Metaxas",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

year = "2019",

month = apr,

doi = "10.1109/ISBI.2019.8759267",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "1865--1868",

booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",

}

Wu, P, Qu, H, Yi, J, Huang, Q, Chen, C & Metaxas, D 2019, Deep attentive feature learning for histopathology image classification. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759267, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, IEEE Computer Society, pp. 1865-1868, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, 04/8/19. https://doi.org/10.1109/ISBI.2019.8759267

Deep attentive feature learning for histopathology image classification. / Wu, Pengxiang; Qu, Hui; Yi, Jingru et al.
ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. p. 1865-1868 8759267 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April).

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

TY - GEN

T1 - Deep attentive feature learning for histopathology image classification

AU - Wu, Pengxiang

AU - Qu, Hui

AU - Yi, Jingru

AU - Huang, Qiaoying

AU - Chen, Chao

AU - Metaxas, Dimitris

PY - 2019/4

Y1 - 2019/4

N2 - In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.

AB - In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.

KW - Attention

KW - Breast

KW - Convolutional neural network

KW - Histopathology image analysis

KW - Transfer learning

UR - https://www.scopus.com/pages/publications/85073899260

U2 - 10.1109/ISBI.2019.8759267

DO - 10.1109/ISBI.2019.8759267

M3 - Conference contribution

AN - SCOPUS:85073899260

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 1865

EP - 1868

BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019

Y2 - 8 April 2019 through 11 April 2019

ER -

Deep attentive feature learning for histopathology image classification

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this