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Identification of Coding Regions in Prokaryotic DNA Sequences Using Bayesian Classification Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Enhancing Breast Cancer Classification via Information and Multi-model Integration

Authors : J. C. Morales, Francisco Carrillo-Perez, Daniel Castillo-Secilla, Ignacio Rojas, Luis Javier Herrera

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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Abstract

The integration of different sources of information for proper classification is of utter importance, specially in the biomedical field. Many different sources of information can be collected from a patient and they all may contribute to an accurate diagnosis. For example in cancer disease these can include gene expression (RNA-Seq) or Tissue Slide Imaging, however, their integration in order to correctly train a classification model is not straightforward. Making use of Whole-Slide-Images, this work presents a novel information integration model when different sources of data from a patient are available, named as Multi-source integration model (MSIM). Using two different Convolutional Neural Networks architectures and a Feed Forward Neural Network, the potential of a multi-model integration process which combines the information of different sources is introduced and its results are presented for Breast Cancer classification.

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previous chapter Medical Image Data Upscaling with Generative Adversarial Networks
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Metadata
Title
Enhancing Breast Cancer Classification via Information and Multi-model Integration
Authors
J. C. Morales
Francisco Carrillo-Perez
Daniel Castillo-Secilla
Ignacio Rojas
Luis Javier Herrera
Copyright Year
2020
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-030-45384-8

Electronic ISBN: 978-3-030-45385-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45385-5_67

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