research-article

Gastrointestinal Tract Diseases Detection with Deep Attention Neural Network

Authors:

Yuan Chang,

Zixuan Huang,

Weizhao Chen,

Qiwei ShenAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 2568 - 2572

https://doi.org/10.1145/3343031.3356061

Published: 15 October 2019 Publication History

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Abstract

Medical image classification and diagnosis is currently a hot topic in the field of deep learning. The ACM International Conference on Multimedia and Simula co-hosted the MutilMedia Grand Challenge, which aims to use artificial intelligence aiding detection and classification of gastrointestinal image. This competition is divided into four subtasks, including detection, efficient detection, efficient detection (the same hardware for all the participants) and report generation. We participate in the multi-label detection task and propose a new attention model, which can effectively improve the network's ability to classify different types of categories. Our approach also uses a series of different techniques including multi-epoch fusion, automatic data augmentation selection, and adaptive threshold selection. Combining these techniques, we are able to achieve good classification results on the given dataset. Finally, our f1 score is 0.907 and MCC is 0.952 with a high speed.

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Cited By

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Tavanapong WOh JRiegler MKhaleel MMittal Bde Groen P(2022)Artificial Intelligence for Colonoscopy: Past, Present, and FutureIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.316009826:8(3950-3965)Online publication date: Aug-2022
https://doi.org/10.1109/JBHI.2022.3160098
Afriyie YA. Weyori BA. Opoku A(2022)Gastrointestinal tract disease recognition based on denoising capsule networkCogent Engineering10.1080/23311916.2022.21420729:1Online publication date: 11-Nov-2022
https://doi.org/10.1080/23311916.2022.2142072
Afriyie YWeyori BOpoku A(2021)Exploring Optimised Capsule Network on Complex Images for Medical Diagnosis2021 IEEE 8th International Conference on Adaptive Science and Technology (ICAST)10.1109/ICAST52759.2021.9682081(1-5)Online publication date: 25-Nov-2021
https://doi.org/10.1109/ICAST52759.2021.9682081
Show More Cited By

Index Terms

Gastrointestinal Tract Diseases Detection with Deep Attention Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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Information & Contributors

Information

Published In

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

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Research-article

Funding Sources

Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Beijing Municipal Natural Science Foundation

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MM '19

Sponsor:

SIGMM

MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

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MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Tavanapong WOh JRiegler MKhaleel MMittal Bde Groen P(2022)Artificial Intelligence for Colonoscopy: Past, Present, and FutureIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.316009826:8(3950-3965)Online publication date: Aug-2022
https://doi.org/10.1109/JBHI.2022.3160098
Afriyie YA. Weyori BA. Opoku A(2022)Gastrointestinal tract disease recognition based on denoising capsule networkCogent Engineering10.1080/23311916.2022.21420729:1Online publication date: 11-Nov-2022
https://doi.org/10.1080/23311916.2022.2142072
Afriyie YWeyori BOpoku A(2021)Exploring Optimised Capsule Network on Complex Images for Medical Diagnosis2021 IEEE 8th International Conference on Adaptive Science and Technology (ICAST)10.1109/ICAST52759.2021.9682081(1-5)Online publication date: 25-Nov-2021
https://doi.org/10.1109/ICAST52759.2021.9682081
Abra Ayidzoe MYu YMensah PCai JAdu KTang Y(2021)Gabor capsule network with preprocessing blocks for the recognition of complex imagesMachine Vision and Applications10.1007/s00138-021-01221-632:4Online publication date: 9-Jun-2021
https://doi.org/10.1007/s00138-021-01221-6

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