CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks

CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks

Abstract Background Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training sampl...

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Bibliographic Details
Main Authors: Ángela Casado-García, César Domínguez, Manuel García-Domínguez, Jónathan Heras, Adrián Inés, Eloy Mata, Vico Pascual
Format: Article
Language:English
Published: BMC 2019-06-01
Series:BMC Bioinformatics
Subjects:
Data augmentation
Classification
Detection
Segmentation
Multi-dimensional images
Online Access:http://link.springer.com/article/10.1186/s12859-019-2931-1

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http://link.springer.com/article/10.1186/s12859-019-2931-1

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