This blog post is based on the net surgery example provided by Caffe. It takes the concept and expands it to a working example to produce pixel-wise output images, generating output in ~2 seconds (simple approach) or ~35 seconds (advanced approach) for a 2,000 x 2,000 image, an improvement from the ~15 hours of a naive pixel wise approach.
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.
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”.
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”.
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”.
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”.
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”.
This blog posts explains how to train a deep learning epithelium segmentation classifier in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.
This blog posts explains how to train a deep learning nuclear segmentation classifier in accordance with our paper “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.
One of the perks of working at Case Western Reserve is that we often qualify for access to cutting edge resource and special projects. In this case, since our digital histology deep learning work requires a large number of GPUs to analyze thousands of patients, we were granted access to the OSC Ruby cluster, which has 20 NVIDIA Tesla K40 GPUs. Since the cluster has only recently been setup, there was some leg work required on our end to get Caffe fully up and running, without root access, which we’ll document here.