Notes

on different stuff

---

Project maintained by dg1an3 Hosted on GitHub Pages — Theme by mattgraham

In this previous post on better image review workflows, David Clunie’s noted zombie apocalypse was imminent as we waited for better predictive models for image review behavior. Well, the zombie apocalypse never happened. But I have made some progress in sketching a basic model to analyze usage patterns.

The inputs are:

• Time of day of physician login / workstation location
• Time of day of image acquisition
• Time offfset of image approval
• Image feature vector, in some latent space of patient geometry

Then these are regressed to get a prediction of likelihood of image being opened on a workstation, at a given time.

What can be done with this prediction? Caching on the local drive could be accomplished using:

• Windows Offline Files
• Dokan library, or similar
• BDS Storage for local drive

Additionally, memory caching could be used

• For a WCF-based SoA architectures, memory caching can be implemented either in the client (using a custom binding stack), or in the service as a custom IOperationInvoker.
• MSQ 3D IRW already implements a memory caching for AVS-based cone beam CTs.

So with some implementation for the actual caching, we can fit the model and produce predictions. If only we had some test data…

Stay tuned for a demo of the model, possibly with only fake input data.