Tag Archives: python

Data Exploration Of Features For Outcome Association In Digital Pathology

Introduction

In the field of digital pathology, a frequent approach for the creation of image-based biomarkers involves extracting features from scanned pathology slides. These features, which are often related to the morphology or spatial distribution of various tissue or cell types, provide valuable insights into the underlying biology of diseases. In cancer research, it is particularly important to examine how these features correlate with clinical outcomes such as overall survival (OS), progression-free survival (PFS), or other binary outcomes (e.g., response to a specific treatment).

Here we release python code that can be executed in a notebook to facilitate this process. It accepts a pandas DataFrame and generates a one-page summary PDF file, facilitating the analysis of individual features and their potential correlation with clinical outcomes.

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Ray: An Open-Source Api For Easy, Scalable Distributed Computing In Python – Part 3 Intro to Serving Models

Through a series of 4 blog posts, we’ll discuss and provide working examples of how one can use the open-source library Ray to (a) scale computing locally (single machine), (b) distribute scaling remotely (multiple-machines), and (c) serve deep learning models across a cluster (2 on this topic, basic/advanced). Please note that the blog posts in this series increasingly raise in difficulty!

This is the second to last blog post in the series, (the first one here, second one here), where we will go into greater detail about how we can use Ray Serve to set up a server waiting to respond to our requests for processing. These last two are the most complex blogpost in the series and require some understanding of how HTTP, REST, and web services work. You can find relevant prereading here.

Ray Serve is a scalable model serving library for building online inference APIs. Serve is framework agnostic, so you can use a single toolkit to serve everything from deep learning models built with frameworks like PyTorch, Tensorflow, and Keras, to Scikit-Learn models, to arbitrary Python business logic.

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Ray: An Open-Source API For Easy, Scalable Distributed Computing In Python – Part 2 Distributed Scaling

Through a series of 4 blog posts, we’ll discuss and provide working examples of how one can use the open-source library Ray to (a) scale computing locally (single machine), (b) distribute scaling remotely (multiple-machines), and (c) serve deep learning models across a cluster (basic/advanced). Please note that the blog posts in this series increasingly raise in difficulty!

This is the second blog post in the series, (the first one here), where we will go into greater detail about how Ray Cluster creation works, associated terminology, requirements for successful execution, and extend our previous local-only example to a distributed environment.

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Ray: An Open-Source Api For Easy, Scalable Distributed Computing In Python – Part 1 Local Scaling

Through a series of 4 blog posts, we’ll discuss and provide working examples of how one can use the open-source library Ray to (a) scale computing locally (single machine), (b) distribute scaling remotely (multiple-machines), and (c) serve deep learning models across a cluster (basic/advanced). Please note that the blog posts in this series increasingly raise in difficulty!

I am personally very excited by the opportunities afforded by Ray, its been a long time desire to have such an easy-to-use library!

Okay, lets start off by talking about scaling local computation with Ray!

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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:

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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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Converting an existing image into an Openslide compatible format

Many digital pathology tools (e.g., our quality control tool, HistoQC), employ Openslide, a library for reading whole slide images (WSI).  Openslide provides a reliable abstraction away from a number of proprietary WSI file-formats, such that a single programmatic interface can be employed to access WSI meta and image data.

Unfortunately, when smaller regions of interest, or new images, are created in tif/png/jpg formats they no longer remain compatible with OpenSlide. This blog post discusses how to take any image and convert it into an OpenSlide compatible WSI, with embedded metadata.

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Computationally creating a PowerPoint presentation of experimental results using Python

This post is an update of the previous post, which discussed how to create a powerpoint slide desk with results using Matlab. In the last couple of years, we have mostly transitioned to python for our digital pathology image analysis, in particular those tasks which employ deep learning. It thus makes sense to port our tools over as well. In this case, we’ll be looking at building powerpoint slide desks using python.

Let’s look at what we want as our final output:

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