The “napari-activelearning” plugin provides a framework to fine tune deep learning models for large-scale bioimage analysis, such as digital pathology Whole Slide Images (WSI). This plugin was developed with the motivation of easing the integration of deep learning tools into bioimage analysis workflows. This plugin implements the concept of Active Learning for reducing the time spent on labeling samples when fine tuning models. Because this plugin is integrated into Napari and leverages the use of next generation file formats (Zarr), it is suitable for fine tuning deep learning models on large-scale images with little image preparation.

Artificial intelligence has been successfully applied to bioimage understanding and achieved significative results in the last decade. Advances in imaging technologies have also allowed the acquisition of higher resolution images. That has increased not only the magnification at what images are captured, but the size of the acquired images as well. This comprises a challenge for deep learning inference in large-scale images, since these methods are commonly used in relatively small regions rather than whole images. This workshop presents techniques to scale-up inference of deep learning models to large-scale image data with help of Dask for parallelization in Python.