DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification
| Autor: | Jónathan Heras, César Domínguez, Vico Pascual, Adrián Inés, Eloy Mata |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Java Computer science Interoperability Health Informatics computer.software_genre 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted Plug-in Dissemination computer.programming_language Point (typography) Contextual image classification business.industry Deep learning Computer Science Applications 030104 developmental biology Table (database) Programming Languages Artificial intelligence Neural Networks Computer Software engineering business computer 030217 neurology & neurosurgery |
| Zdroj: | Computers in biology and medicine. 108 |
| ISSN: | 1879-0534 |
| Popis: | Background and objective Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. However, the models created from deep learning frameworks cannot be easily accessed from bioimaging tools such as ImageJ or Icy; this means that life scientists are not able to take advantage of the results obtained with those models from their usual tools. In this paper, we aim to facilitate the interoperability of bioimaging tools with deep learning frameworks. Methods In this project, called DeepClas4Bio, we have developed an extensible API that provides a common access point for classification models of several deep learning frameworks. In addition, this API might be employed to compare deep learning models, and to extend the functionality of bioimaging programs by creating plugins. Results Using the DeepClas4Bio API, we have developed a metagenerator to easily create ImageJ plugins. In addition, we have implemented a Java application that allows users to compare several deep learning models in a simple way using the DeepClas4Bio API. Moreover, we present three examples where we show how to work with different models and frameworks included in the DeepClas4Bio API using several bioimaging tools — namely, ImageJ, Icy and ImagePy. Conclusions This project brings to the table benefits from several perspectives. Developers of deep learning models can disseminate those models using well-known tools widely employed by life-scientists. Developers of bioimaging programs can easily create plugins that use models from deep learning frameworks. Finally, users of bioimaging tools have access to powerful tools in a known environment for them. |
| Databáze: | OpenAIRE |
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