I would be interested to form a team.
Although this is the first time i try out x-ray images. (It resonates with me very well. Using AI to help people is the reason I am into it)
I have been thinking about a couple of options, one might combine them perhaps in the end as an ensemble of sorts.
Since x-ray are grayscale, you would not need an RGB tensor, but i am thinking one could combine all the views (images) into one tensor. One problem is that there are different amounts of images per study. Also, some of the x rays were white while others were black, so perhaps one would need to normalize these by inverting.
Many to one classification, RNN or equivalent.
Averaging the results of each image as in the paper.
Adding Embeddings for the extremity type, wrist, shoulder etc…
Although this might be picked up anyway by the network, i am not sure if it is needed.
Averaging 1-4 to give final result
tell me if this doesn’t make sense.
@alexandrecc, with 3D correlation, do you mean that if there is a probable abnormality in let’s say index finger middle joint, on image 1, if image 2 also has a probable abnormality is in the same place (from a different angle) it would consider that abnormality to have higher importance. Or do you mean that in ones mind you create “layers” in 3D from the 2D image