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First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

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First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

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García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V. (2019). First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. Entropy. 21(4). https://doi.org/10.3390/e21040356

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/124304

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Title: First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve ...[+]
Subjects: Gland classification , Hand-crafted feature extraction , Feature selection , Hand-driven learning , Deep learning , Prostate cancer , Histological image
Copyrigths: Reconocimiento (by)
Source:
Entropy. (issn: 1099-4300 )
DOI: 10.3390/e21040356
Publisher:
MDPI AG
Publisher version: http://doi.org/10.3390/e21040356
Conference name: XXXVI Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2018)
Conference place: Ciudad Real, España
Conference date: Noviembre 21-23,2018
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 Gabriel Garcia has been supported by the State Research Spanish Agency PTA2017-14610-I.[+]
Type: Artículo Comunicación en congreso

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