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Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

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Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

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García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MÁ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/124077

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Title: Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques
Author: García-Pardo, José Gabriel Colomer, Adrián Naranjo Ornedo, Valeriana Peñaranda, Francisco Sales, María Ángeles
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied ...[+]
Subjects: Machine learning , Multilayer perceptron , Support vector machine , Locally constrained watershed transform , Gland unit identification , Histological prostate image
Copyrigths: Reserva de todos los derechos
ISBN: 978-3-030-03492-4
Source:
Intelligent Data Engineering and Automated Learning – IDEAL 2018.
DOI: 10.1007/978-3-030-03493-1_67
Publisher:
Springer
Publisher version: http://dx.doi.org/10.1007/978-3-030-03493-1_67
Conference name: International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Conference place: Madrid, Spain
Conference date: Noviembre 21-23,2018
Series: Lecture Notes in Computer Science;11314
Project ID:
info:eu-repo/grantAgreement/MINECO//DPI2016-77869-C2-1-R/ES/SISTEMA DE INTERPRETACION DE IMAGENES HISTOPATOLOGICAS PARA LA DETECCION DE CANCER DE PROSTATA/
info:eu-repo/grantAgreement/MINECO//BES-2014-067889/ES/BES-2014-067889/
Thanks:
This work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889. We ...[+]
Type: Capítulo de libro Comunicación en congreso

References

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Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)

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Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)

Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)

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Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)

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