In my previous blog post “Learning Deep Learning”, I showed how to use the KNIME Deep Learning - DL4J Integration to predict the handwritten digits from images in the MNIST dataset. That’s a neat trick, but it’s a problem that has been pretty well solved for a while. What about trying something a bit more difficult? In this blog post I’ll take a dataset of images from three different subtypes of lymphoma and classify the image into the (hopefully) correct subtype.
KNIME Deep Learning - Keras Integration brings new deep learning capabilities to KNIME Analytics Platform. You can now use the Keras Python library to take advantage of a variety of different deep learning backends. The new KNIME nodes provide a convenient GUI for training and deploying deep learning models while still allowing model creation/editing directly in Python for maximum flexibility.
The workflows mentioned in this blogpost require a fairly heavy amount of computation (and waiting), so if you’re just looking to check out the new integration, see the simple workflow here that recapitulates the results of the previous blog post using the new Keras Integration. There are quite a few more example workflows for both DL4J and Keras which can be found on the KNIME Hub.
Right, back to the challenge. Malignant lymphoma affects many people, and among malignant lymphomas, CLL (chronic lymphocytic leukemia), FL (follicular lymphoma), and MCL (mantle cell lymphoma) are difficult for even experienced pathologists to accurately classify.A typical task for a pathologist in a hospital would be to look at those images and make a decision about what type of lymphoma is present. In many cases, follow-up tests to confirm the diagnosis are required. An assistive technology that can guide the pathologist and speed up their job would be of great value. Freeing up the pathologist to spend their time on those tasks that computers can’t do so well, has obvious benefits for the hospital, the pathologist, and the patients.
Figure 1. The modeling process adopted to classify lymphoma images. At each stage the required components are listed.