JDLL: a library to run deep learning models on Java bioimage informatics platforms

The advancements in artificial intelligence (AI) technology over the past decade have been a breakthrough in imaging for life sciences, paving the way for novel methods in image restoration1, reconstruction2 and segmentation3. However, the wide adoption of deep learning (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 (for example, TensorFlow 1 or 2, PyTorch) that come with distinct and often conflicting setups, which can discourage even proficient developers. This has led to integration difficulties or even absence in mainstream bioimage informatics platforms such as ImageJ, Icy and Fiji, many of which are primarily developed in Java.

JDLL: a library to run deep learning models on Java bioimage informatics platforms Articles