Category Archives: Digital Histology

Download TCGA Digital Pathology Images (FFPE)

Digital pathology image analysis requires high quality input images. While there are a large number of images available in The Cancer Genome Atlas (TCGA), the ones which are currently available in the data portal are frozen specimens and are *not* suitable for computational analysis. This post discusses how to download the Formalin-Fixed Paraffin-Embedded (FFPE) slides for corresponding patients.

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On Stain Normalization in Deep Learning

Just wanted to take a moment and share some quick stain normalization type experimental results. We have a trained in-house nuclei segmentation model which works fairly well when the test images have similar stain presentation properties, but when new datasets arrive which are notably different we tend to see a decreased classifier performance.

Here we look at one of these images and ways of improving classifier robustness.

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Real time Data Augmentation using Nvidia Digits + Python Layer

One of the common ways of increasing the size of a training set is to augment the original data with a set of modified patches. These modifications often include (a) rotations, (b) mirroring, (c) lighting adjustment, (d) affine transformations (sheering, etc), (e) magnification modification, (f) addition of noise, etc. This blog post discusses how to do the most trivial modification, rotation, in real-time using a python layer through Nvidia Digits. Given this code, it should be easy to add on other desired augmentations.

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Revised Deep Learning approach using Matlab + Caffe + Python

Our publication “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases” , showed how to use deep learning to address many common digital pathology tasks. Since then, many improvements have been made both in the field and in my implementation of them. In this blog post, I re-address the nuclei segmentation use case using the latest and greatest approaches.

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Dividing and re-merging large images (Humpty Dumpty)

One of the challenges in working in digital pathology is that the associated images can be excessively large, too large to load fully into memory, as well as too large to use in common pipelines. For example, a Aperio SVS file that we’ll look at today is 60,000 x 42,600 pixels. If we tried to load such an image, in RGB space, uncompressed it would require ~7GB, making it too large to consider using in our deep learning pipelines as there wouldn’t be enough RAM on the GPU for both the data and the filter activations.

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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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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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