Tag Archives: segmentation

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].

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Digital Pathology Segmentation using Pytorch + Unet

In this blog post, we discuss how to train a U-net style deep learning classifier, using Pytorch, for segmenting epithelium versus stroma regions. This post is broken down into 4 components following along other pipeline approaches we’ve discussed in the past:

  1. Making training/testing databases,
  2. Training a model,
  3. Visualizing results in the validation set,
  4. Generating output.

This model focuses on using solely Python and freely available tools (i.e., no matlab).

This blog post assumes moderate knowledge of convolutional neural networks, depending on the readers background, our JPI paper may be sufficient, or a more thorough resource such as Andrew NG’s deep learning course.

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Tutorial: A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images

This tutorial provides a tutorial on using the code and data for our paper “A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images” by Andrew Janowczyk, Scott Doyle, Hannah Gilmore, and Anant Madabhushi.

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