I’m very much a HTM newbie and trying to read as many papers as I can, so go easy on me!
I’d like to experiment with HTM on CT scan sequences of lungs, to see if it can detect a nodule (tumor) within a CT scan. I’m aware you can achieve this with CNN.
I was thinking:
- a full CT scan (comprised of all slices - from the top of the lungs to the bottom) could represent a single pattern to be learned
- make the CT scan artificially temporal by transforming it from series of images into video format
- do any necessary preprocessing - binark mask, normalisation
- encode the data into SDRs and feed to the temporal pooler
- after training, introduce a CT scan with an anomaly (nodule)
- could this work even if it is artifically temporal? will it not work because HTM predicts at every time step?
- would there be too much variation between scans (scans from different people, not the same pair of lungs every time - could it still learn the pattern?)
- how many training instances would it require?
- would this require some serious resources to run, or will that depend on how much I can reduce the size of the input data?
Thank you very much in advance!