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.继续阅读 使用Paquo直接与QuPath项目文件交互，以用于数字病理学机器学习
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.继续阅读 教程：用于小管分割的快速注释器
当前GPU的利用率通常受到快速将数据进出设备的能力的限制。更准确地说，这意味着从主机RAM获取数据，通过PCI-e总线将其传输到GPU RAM是许多深入学习用例的瓶颈。继续阅读 通过压缩将数据更快地传输到GPU
Update-Nov 2020: Code has now been placed in github which enables the reading and writing of compressed geojson files at all stages of the process described below. Compression reduces the file size by approximately 93% : )
QuPath is a digital pathology tool that has become especially popular because it is both easy to use to and supports a large number of different whole slide image (WSI) file formats. QuPath is also able to perform a number of relevant analytical functions with a few mouse clicks. Of interest in this blog post is mentioning that the pathologists we tend to work with are either already familiar with QuPath, or find it easier to learn versus other tools. As a result, QuPath has become a goto tool for us for both the creation, and review of, annotations and outputs created by our algorithms.
Here we introduce a robust method using GeoJSON for exporting annotations (or cell objects) from QuPath, importing them into python as shapely objects, operating upon them, and then re-importing a modified version of them back into QuPath for downstream usage or review. As an example use case we will be looking at computationally identifying lymphocytes in WSIs of melanoma metastases using a deep learning classifier.继续阅读 Exporting and re-importing annotations from QuPath for usage in machine learning
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:继续阅读 Computationally creating a PowerPoint presentation of experimental results using Python