Learning to Learn

End-to-End Medical Image AI: From Raw DICOM to Clinical Deployment End-to-End Medical Image AI: From Raw DICOM to Clinical Deployment There is a question worth sitting with before writing a single line of code: when a radiologist reads a CT scan, they do not run three separate mental programs — one for loading the DICOM files, another for deciding which Hounsfield window to apply, a third for actually spotting the tumour. Something more practised happens. A trained visual system and decades of anatomical knowledge combine seamlessly into a single interpretive act. The pixels become a diagnosis. Building a machine that replicates even a fraction of that process requires solving a surprising number of sub-problems in the right order. This post walks through every stage of the pipeline — from where to get publicly available MRI and CT data, through preprocessing, model architecture, loss functions, training mechanics, and all the way to clinical deployment and ongoing mon...