Fig. 2: Quantitative Imaging Feature Pipeline.
From: Artificial intelligence and machine learning in cancer imaging
This shows an example of the quantitative imaging feature pipeline (QIFP) used to process a positron emission tomography (PET) imaging cohort stored on a local network ePAD server. The box next to the “modify workflow” button is a selection button, which has been set to choose the workflow displayed. This workflow moves the image data into Stanford’s Quantitative Image Feature Engine (QIFE)64, which computes thousands of image features for each segmented tumour in the cohort, followed by a sparse regression modeler (LASSO TRAIN) that derives an association between a linear combination of a small number of image features to 5-year survival, and finally tests that model in an unseen cohort and produces an ROC curve displaying the accuracy of the association. Other workflows can be chosen that use one or more of the existing tools stored on the QIFP system.
