Category Archives: Digital Histology

Use Case 6: Invasive Ductal Carcinoma (IDC) Segmentation

This blog posts explains how to train a deep learning Invasive Ductal Carcinoma (IDC) classifier in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.

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Use Case 4: Lymphocyte Detection

Typically, you’ll want to use a validation set to determine an optimal threshold as it is often not .5 (which is equivalent to argmax). Subsequently, use this threshold on the the “_prob” image to generate a binary image.This blog posts explains how to train a deep learning lymphocyte detector in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.

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Exporting from Matlab To PowerPoint

Reviewing the results of an image based experiment, across many images, can be annoying in matlab. Too much clicking!

I’ve recently started using PowerPoint to view many of my results. This blog posts discuss how using the free export to PowerPoint toolbox it is possible to create a slide desk with all relevant information for easier viewing. It looks like this:


image1_annotated_trimmed

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Exporting Bisque Gobjects/Annotations as Binary Masks into Matlab

Annotations which stay solely in Bisque aren’t incredibly useful. One of the main reasons for marking up images is to be able to use those annotations for some other purpose, such as training classifiers, computing metrics and features. In this post, we show how to take the annotations from bisque via REST, and convert them into binary masks in matlab.

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