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دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification
ترجمه فارسی عنوان مقاله:
DeepClas4Bio: اتصال ابزارهای تصویربرداری با چارچوبهای یادگیری عمیق برای طبقه بندی تصویر
منبع:
Sciencedirect - Elsevier - Computers in Biology and Medicine, 108 (2019) 49-56: doi:10:1016/j:compbiomed:2019:03:026
نویسنده:
A. Inés∗, C. Domínguez, J. Heras, E. Mata, V. Pascual
چکیده انگلیسی:
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.
Keywords: Deep learning | Bioimaging | Image classification | Interoperability
قیمت: رایگان
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