Digital pathology projects often require assigning a class to cells/objects. For example, you may have a segmentation of cells/glomeruli/tubules and want to identify the ones which are lymphocytes/sclerotic/distal. This classification process can be done using machine or deep learning classifiers by supplying the object of question and receiving an output score which indicates the likelihood that that particular object is of that particular type.
This blog post will demonstrate an efficient way of using QuPath to help find the ideal likelihood threshold for your classifier.
Continue reading Using QuPath To Help Identify An Optimal Threshold For A Deep Or Machine Learning Classifier
This is an updated version of the previously described workflow on how to load and classify annotations/detections created in QuPath for usage in downstream machine learning workflows. The original post described how to use the Groovy programming language used by QuPath to export annotations/detections as GeoJSON from within QuPath, made use of a Python script to classify them, and lastly used another Groovy script to reimport them. If you are not familiar with QuPath and/or its annotations you should probably read the original post first to provide better context and understanding of the respective workflows, as well as being able to appreciate the more elegant approach taken here. If you are already using the described approach, you should be able to easily modify it to follow this newer approach.
Continue reading Using Paquo to directly interact with QuPath project files for usage in digital pathology machine learning