Overview¶
Deep learning has transformed many aspects of industrial pipelines recently. Scientists involved in biomedical imaging research are also benefiting from the power of AI to tackle complex challenges. Although academic community has widely accepted image processing tools, such as scikit-image, ImageJ, there is still a need for a tool which integrates deep learning into biomedical image analysis. We propose a minimal, but convenient Python package based on PyTorch with biomedical datasets, common deep learning models, and extended by flexible trainers.
What can I do with this package?¶
Load public biomedical datasets
Load common deep learning models
Do basic image preprocessing and transformations
Customize training loops to your own needs
Package structure¶
![graph farabioOverview {
node [shape=box, colorscheme=set32 , style=rounded];
farabio -- core;
farabio -- data;
farabio -- models;
farabio -- utils;
farabio [fillcolor=1, style="rounded"]
core [fillcolor=2, style="rounded"]
data [fillcolor=2, style="rounded"]
models [fillcolor=2, style="rounded"]
utils [fillcolor=2, style="rounded"]
}](../_images/graphviz-f7c2a07e0fc535fd8860802ccb7b007d6d668470.png)
How to contribute?¶
You can contribute to this package by reporting issues and/or by sending pull request.
If you find a bug, please report it by opening an issue on Git.