Instead of transferring large amounts of image data, this post discusses how to set up a centralized Omero server for remote access by QuPath, to enable real-time whole slide image reviewing and annotating!
Continue reading Using OMERO in QuPath to have remote access to your dataTag Archives: digital pathology
Approach for Easy Visual Comparison between ground-truth and predicted classes
Although classification metrics are good for summarizing a model’s performance on a dataset, they disconnect the user from the data itself. Similarly, a confusion matrix might tell us that performance is suffering because of false positives, but it obscures information about what patterns may have caused those misclassifications and what types of false positives there might be.
One way to gain interpretability is to group sampled images by the category of their output (true negative, false negative, false positive, true positive), and display them in a powerpoint file for facile review. These visualizable categories make it easy to identify patterns in misclassified data that can be exploited to improve performance (e.g., hard negative mining, or image analysis based filtering).
This blog post describes and demonstrates a workflow that produces such a powerpoint slide deck automatically for review, as shown below:
Using QuPath To Help Identify An Optimal Threshold For A Deep Or Machine Learning Classifier
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.
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How to Select the Correct Magnification and Patch Size for Digital Pathology Projects
In digital pathology, input data is often exceedingly too large for DL models to process directly, with Whole Slide Images (WSI) around 100k x 100k pixels. This post provides a quantitative and qualitative method, with code, to help optimize important digital pathology specific hyperparameters: patch size and magnification. Optimizing these variables can decrease training times, lowers hardware requirements, and reduces the amount of data required to effectively train a model.
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Application of ICC profiles to digital pathology images
Background on Color Calibration
Digital whole slide image scanners are designed to take stained tissue on glass slides and digitize them into bytes for usage in the digital world. The process by which slide scanners perform this operation does not produce a perfect digital equivalent of the original slide as the hardware involved (led/blub, camera sensor, quantizer) can introduce some biases during the sampling process. For example, different camera sensors may detect colors with different levels of specificity/accuracy/density, resulting in similar but not perfect representations of the associated real-world subjects.
Concretely, there is often a difference between the color you perceive in the real-world under a microscope versus what you would see if you looked at the corresponding digital copy of the same slide. This blog post discusses how to correct for this discrepancy using ICC profiles.
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Using Paquo to directly interact with QuPath project files for usage in digital pathology machine learning
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.
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Tutorial: Quick Annotator for Tubule Segmentation
The manual labeling of large numbers of objects is a frequent occurrence when training deep learning classifiers in the digital histopathology domain. Often this can become extremely tedious and potentially even insurmountable.
To aid people in this annotation process we have developed and released Quick Annotator (QA), a tool which employs a deep learning backend to simultaneously learn and aid the user in the annotation process. A pre-print explaining this tool in more detail is available [here].
De-array a Tissue Microarray (TMA) using QuPath and Python
This blogpost demonstrats how to use QuPath and Python to de-array a tissue microarray (TMA) for computational analysis and was co-authored by Fan Fan (fxf109@case.edu)
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The noise in our digital pathology slides
In adding new features to HistoQC , I stumbled upon a very interesting insight that I thought I would take a moment to share. The amount of noise and artifacts in digital pathology (DP) whole slide images (WSI) is far more extensive than I had previously thought.
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