Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization

Mahfujul Islam Rumman; Naoaki Ono; Kenoki Ohuchida; Md Altaf-Ul-Amin; Ming Huang; Shigehiko Kanaya

doi:10.1109/GCCE56475.2022.10014057

Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization

Mahfujul Islam Rumman, Naoaki Ono, Kenoki Ohuchida, Md Altaf-Ul-Amin, Ming Huang, Shigehiko Kanaya

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

Abstract

In recent years, computer-aided diagnosis based on deep learning concepts has become an attractive research topic in medical imaging. Most of these works utilized supervised learning methods that required prior pathological knowledge. However, it is necessary to extract potential features in images, based on unsupervised learning in order to obtain new pathological findings. For this reason, we implemented unsupervised cluster analysis based on maximization of mutual information to classify pathological images of pancreatic cancer into discrete categories.

Original language	English
Title of host publication	GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	117-120
Number of pages	4
ISBN (Electronic)	9781665492324
DOIs	https://doi.org/10.1109/GCCE56475.2022.10014057
Publication status	Published - 2022
Event	11th IEEE Global Conference on Consumer Electronics, GCCE 2022 - Osaka, Japan Duration: Oct 18 2022 → Oct 21 2022

Publication series

Name	GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics

Conference

Conference	11th IEEE Global Conference on Consumer Electronics, GCCE 2022
Country/Territory	Japan
City	Osaka
Period	10/18/22 → 10/21/22

All Science Journal Classification (ASJC) codes

Signal Processing
Information Systems and Management
Electrical and Electronic Engineering
Media Technology
Instrumentation
Social Psychology

UN SDGs

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

Access to Document

10.1109/GCCE56475.2022.10014057

Cite this

Rumman, M. I., Ono, N., Ohuchida, K., Altaf-Ul-Amin, M., Huang, M., & Kanaya, S. (2022). Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization. In GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics (pp. 117-120). (GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE56475.2022.10014057

Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization. / Rumman, Mahfujul Islam; Ono, Naoaki; Ohuchida, Kenoki et al.

GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc., 2022. p. 117-120 (GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics).

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

Rumman, MI, Ono, N, Ohuchida, K, Altaf-Ul-Amin, M, Huang, M & Kanaya, S 2022, Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization. in GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics. GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics, Institute of Electrical and Electronics Engineers Inc., pp. 117-120, 11th IEEE Global Conference on Consumer Electronics, GCCE 2022, Osaka, Japan, 10/18/22. https://doi.org/10.1109/GCCE56475.2022.10014057

Rumman MI, Ono N, Ohuchida K, Altaf-Ul-Amin M, Huang M, Kanaya S. Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization. In GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc. 2022. p. 117-120. (GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics). doi: 10.1109/GCCE56475.2022.10014057

Rumman, Mahfujul Islam ; Ono, Naoaki ; Ohuchida, Kenoki et al. / Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization. GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 117-120 (GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics).

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abstract = "In recent years, computer-aided diagnosis based on deep learning concepts has become an attractive research topic in medical imaging. Most of these works utilized supervised learning methods that required prior pathological knowledge. However, it is necessary to extract potential features in images, based on unsupervised learning in order to obtain new pathological findings. For this reason, we implemented unsupervised cluster analysis based on maximization of mutual information to classify pathological images of pancreatic cancer into discrete categories.",

author = "Rumman, {Mahfujul Islam} and Naoaki Ono and Kenoki Ohuchida and Md Altaf-Ul-Amin and Ming Huang and Shigehiko Kanaya",

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Feature Extraction and Unsupervised Clustering of Histopathological Images of Pancreatic Cancer Using Information Maximization

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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