clementiasparrow

clementiasparrow t1_iw0if2t wrote

Try to find a problem (maybe within computer vision) that you care about and where you have labelled data. Then maybe try to implement ( i.e. copy and paste) basic versions of standard architectures and start training. It probably doesn’t perform well to begin with so you start fiddling with regularization and losses and layers and features and it gets better. If you feel a rush, you get the energy to carry on, watch youtube tutorials, take coursera courses and maybe even read papers. You are on the path to develop the practical wisdom that drives research and applications these days. Its all about getting you hands dirty. All those fancy looking papers are not a result of theoretical thoughts and careful planing. Rather, they had some ideas and started coding. It looked promising but it didnt work, but then they fiddled and got some more ideas. And it improved - and at some point, they had something they could publish.

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clementiasparrow t1_iutfviu wrote

I know nothing about this field but here is an idea a bit out of the ordinary. I couldn’t help it :)

If you can get instance segmentation to work on fibers in a voxel of some small size, you could dice it into subvoxels and create a graph that links fiber segments to other segments of the same fiber in other neighboring voxels. Now it becomes a link-prediction problem on a graph. If you can associate some mask with the fiber segments through instace segmentation, you could feed a. the local voxel-coordinates, b. the local image data and c. the mask as node-data for each fiber-sement-node. Now you could try some fancy GNN link predictors on the local area of voxels and see if it will connect fiber segments correctly.

Hope you at least got a good laugh and good luck!

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