This brief blog post sees a modified release of the previous segmentation and classification pipelines. These versions leverage an increasingly popular augmentation library called albumentations.
Tag Archives: tutorial
Visualizing DenseNet Using PyTorch
Deep learning (DL) models have been performing exceptionally well on a number of challenging tasks lately. Unfortunately, given the current blackbox nature of these DL models, it is difficult to try and “understand” what the network is seeing and how it is making its decisions. Building upon our previous post discussing how to train a DenseNet for classification, we discuss here how to apply various visualization techniques to enable us to interrogate the network. The code here is designed as drop-in functionality for any network trained using the previous post, hopefully easing the burden of its implementation.
Digital pathology classification using Pytorch + Densenet
In this blog post, we discuss how to train a DenseNet style deep learning classifier, using Pytorch, for differentiating between different types of lymphoma cancer. This post and code are based on the post discussing segmentation using U-Net and is thus broken down into the same 4 components:
- Making training/testing databases,
- Training a model,
- Visualizing results in the validation set,
- Generating output.
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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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How to Install Nvidia Digits
Here we discuss how to install Nvidia Digits. This is mostly intended as a documentation of the process I had to go through to install it in my lab environment on a single stand-alone machine housing 3 gpus.
Use Case 7: Lymphoma Sub-Type Classification
This blog posts explains how to train a deep learning lymphoma sub-type 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 5: Mitosis Detection
This blog posts explains how to train a deep learning mitosis detector in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.
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 3: Tubule Segmentation
This blog posts explains how to train a deep learning tubule segmentation classifier in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.
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”.