Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi

Onur Can Koyun; Gokhan Bilgin

doi:10.1109/BIYOMUT.2017.8479149

Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi

Translated title of the contribution: Nuclei detection in histopathological images with deep learning and heuristic optimization

Onur Can Koyun, Gokhan Bilgin

Yildiz Technical University

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

1 Citation (Scopus)

Abstract

Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H & E) stained histopathology images of colorectal adenocarcinoma.

Translated title of the contribution	Nuclei detection in histopathological images with deep learning and heuristic optimization
Original language	Turkish
Title of host publication	2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538653401
DOIs	https://doi.org/10.1109/BIYOMUT.2017.8479149
Publication status	Published - 1 Oct 2018
Externally published	Yes
Event	21st National Biomedical Engineering Meeting, BIYOMUT 2017 - Istanbul, Turkey Duration: 24 Nov 2017 → 26 Nov 2017

Publication series

Name	2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017

Conference

Conference	21st National Biomedical Engineering Meeting, BIYOMUT 2017
Country/Territory	Turkey
City	Istanbul
Period	24/11/17 → 26/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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10.1109/BIYOMUT.2017.8479149

Cite this

Koyun, O. C., & Bilgin, G. (2018). Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi. In 2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017 Article 8479149 (2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIYOMUT.2017.8479149

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abstract = "Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H & E) stained histopathology images of colorectal adenocarcinoma.",

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Koyun, OC & Bilgin, G 2018, Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi. in 2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017., 8479149, 2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017, Institute of Electrical and Electronics Engineers Inc., 21st National Biomedical Engineering Meeting, BIYOMUT 2017, Istanbul, Turkey, 24/11/17. https://doi.org/10.1109/BIYOMUT.2017.8479149

Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi. / Koyun, Onur Can; Bilgin, Gokhan.
2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. 8479149 (2017 21st National Biomedical Engineering Meeting, BIYOMUT 2017).

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

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AB - Pathological examinations play a critical role in the diagnosis process. Pathologists analyze biopsies to make a diagnosis. However, detection of nuclei in histopathology images is a costly procedure in terms of time. Because of the complexity of histopathology images, different observers might reach different conclusions. Recently, automatic digital pathology, which is faster therefore beneficial for patients and pathologists, draw many attention for research and clinical practice. In comparison to manual image analysis, computerized methods are not affected by the inter-observer variations. In this paper, we automated the nuclei detection process using deep convolutional neural networks (CNN) and simulated annealing to find center coordinates of nuclei in hematoxylin and eosin (H & E) stained histopathology images of colorectal adenocarcinoma.

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M3 - Konferans katkısı

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Derin Öǧrenme ve Sezgisel Optimizasyon ile Histopatolojik Görüntülerde Çekirdek Merkezlerinin Bulunmasi

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