JDLL: A Library to Run Deep Learning Models on Java Bioimage Informatics Platforms
C. García-López-de-Haro, S. Dallongeville, T. Musset, E. Gómez-de-Mariscal, D. Sage, W. Ouyang, A. Muñoz-Barrutia, J.-Y. Tinevez, J.-C. Olivo-Marin
Nature Methods, in press.
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The advancements in Artificial Intelligence (AI) technology over the past decade have been a breakthrough on imaging for life sciences, paving the way for novel methods in image restoration , reconstruction , and segmentation . However, the wide adoption of DL techniques by end-users in bioimage analysis is hindered by the complexity of their deployment. These techniques stem from a variety of rapidly evolving frameworks (e.g., TensorFlow 1 or 2, Pytorch, etc.) that come with distinct and often conflicting setups, which can discourage even proficient developers. Consequently, this has led to integration hassles or even absence in mainstream bioimage informatics platforms such as ImageJ, Icy, and Fiji, many of which are primarily developed in Java.
M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, E.W. Myers, "Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy," Nature Methods, vol. 15, pp. 1090-1097, December, 2018.