Abstract
Nowadays, deep learning techniques are playing an important role in different areas due to the fast increase in both computer processing capacity and availability of large amount of data. Their applications are diverse in the field of bioimage analysis, e.g. for classifying and segmenting microscopy images, for automating the localization of proteins or for automating brain MRI segmentation. Our goal in this project consists in including these deep learning techniques in ImageJ – one of the most used image processing programs in this research community. To do this, we want to develop an ImageJ plugin from which to use the models and functionalities of the main deep learning frameworks (such as Caffe, Keras or Tensorflow). It would be feasible to test the suitability of different models to the problem that is being studied at each moment, avoiding the problems of interoperability among different frameworks. As a first step, we will define an API that allows the invocation of deep models for object classification from several frameworks; and, subsequently, we will develop an ImageJ plugin to make the use of such an API easier.
This work was partially supported by Ministerio de Economía, Industria y Competitividad [MTM2014-54151-P, MTM2017-88804-P], and Agencia de Desarrollo Económico de La Rioja [ADER-2017-I-IDD-00018].
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Inés, A., Domínguez, C., Heras, J., Mata, E., Pascual, V. (2019). Towards Integrating ImageJ with Deep Biomedical Models. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_40
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