Deep learning for visual recognition of environmental enteropathy and celiac disease

Aman Shrivastava; Karan Kant; Saurav Sengupta; Sung Jun Kang; Marium Khan; S. Asad Ali; Sean R. Moore; Beatrice C. Amadi; Paul Kelly; Donald E. Brown; Sana Syed

doi:10.1109/BHI.2019.8834458

Deep learning for visual recognition of environmental enteropathy and celiac disease

Aman Shrivastava
, Karan Kant
, Saurav Sengupta
, Sung Jun Kang
, Marium Khan
, S. Asad Ali
, Sean R. Moore
, Beatrice C. Amadi
, Paul Kelly
, Donald E. Brown
, Sana Syed

Department of Community Health Sciences, Medical College, Pakistan

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

10 Citations (Scopus)

Abstract

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.

Original language	English (US)
Title of host publication	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728108483
DOIs	https://doi.org/10.1109/BHI.2019.8834458
Publication status	Published - May 2019
Event	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States Duration: 19 May 2019 → 22 May 2019

Publication series

Name	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Country/Territory	United States
City	Chicago
Period	19/05/19 → 22/05/19

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/BHI.2019.8834458

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Shrivastava, A., Kant, K., Sengupta, S., Kang, S. J., Khan, M., Ali, S. A., Moore, S. R., Amadi, B. C., Kelly, P., Brown, D. E., & Syed, S. (2019). Deep learning for visual recognition of environmental enteropathy and celiac disease. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings Article 8834458 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834458

Shrivastava, Aman ; Kant, Karan ; Sengupta, Saurav et al. / Deep learning for visual recognition of environmental enteropathy and celiac disease. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

@inproceedings{809802b0c368474a8ecab5e8c072c581,

title = "Deep learning for visual recognition of environmental enteropathy and celiac disease",

abstract = "Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90\% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.",

author = "Aman Shrivastava and Karan Kant and Saurav Sengupta and Kang, \{Sung Jun\} and Marium Khan and Ali, \{S. Asad\} and Moore, \{Sean R.\} and Amadi, \{Beatrice C.\} and Paul Kelly and Brown, \{Donald E.\} and Sana Syed",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 ; Conference date: 19-05-2019 Through 22-05-2019",

year = "2019",

month = may,

doi = "10.1109/BHI.2019.8834458",

language = "English (US)",

series = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",

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

booktitle = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",

address = "United States",

}

Shrivastava, A, Kant, K, Sengupta, S, Kang, SJ, Khan, M, Ali, SA, Moore, SR, Amadi, BC, Kelly, P, Brown, DE & Syed, S 2019, Deep learning for visual recognition of environmental enteropathy and celiac disease. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings., 8834458, 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019, Chicago, United States, 19/05/19. https://doi.org/10.1109/BHI.2019.8834458

Deep learning for visual recognition of environmental enteropathy and celiac disease. / Shrivastava, Aman; Kant, Karan; Sengupta, Saurav et al.
2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8834458 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

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

TY - GEN

T1 - Deep learning for visual recognition of environmental enteropathy and celiac disease

AU - Shrivastava, Aman

AU - Kant, Karan

AU - Sengupta, Saurav

AU - Kang, Sung Jun

AU - Khan, Marium

AU - Ali, S. Asad

AU - Moore, Sean R.

AU - Amadi, Beatrice C.

AU - Kelly, Paul

AU - Brown, Donald E.

AU - Syed, Sana

PY - 2019/5

Y1 - 2019/5

N2 - Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.

AB - Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.

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U2 - 10.1109/BHI.2019.8834458

DO - 10.1109/BHI.2019.8834458

M3 - Conference contribution

AN - SCOPUS:85073027240

T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019

Y2 - 19 May 2019 through 22 May 2019

ER -

Shrivastava A, Kant K, Sengupta S, Kang SJ, Khan M, Ali SA et al. Deep learning for visual recognition of environmental enteropathy and celiac disease. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8834458. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). doi: 10.1109/BHI.2019.8834458

Deep learning for visual recognition of environmental enteropathy and celiac disease

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