[ebg13]

A lot of people here are rightly concerned about the dangers of falsely marking something as an artifact, but let me present additional data that will hopefully sway you a little bit...

If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There are computational filtering techniques for reducing these artifacts, but, respectfully, they are still really terrible, and close to the implants you can't see shit. All advancements in this area short of inventing new imaging physics will most likely be purely computational corrections. Consider that.

[efournie]

Computational filtering techniques are difficult for a good reason. In the case of CT, high density objects like metal implants produce beam hardening by preventing the low energy photons from reaching the detector. With adversarial training, you can train a network to recognize and remove the artifacts, but you won't be able to reconstruct structures for which there is no physical measurement.

There were similar discussions a few year ago when deep learning was not commonly used yet and compressed sensing was the hot topic of the moment. It can reconstruct MRI or CT images from limited data (and thus allows for quick MR scans or low dose CT) but you have to satisfy a sparsity condition that is seldom granted. There are a few use cases (like MR angiography) where the data is sparse enough and compressed sensing works great.

For deep learning techniques, you need to be very cautious about which structures your network may remove or introduce.

[wigl]

I remember coming across this in a kaggle challenge: https://twitter.com/jeremyphoward/status/1184453643287683072...

[vardump]

I think I'd prefer radiologists use both computationally filtered and this. Computational filtering has also advanced over the years.

[ebg13]

This _is_ computational filtering. It's not philosophically any different. Every filtering method algorithmically guesses what's important or what's real and what's not.

[creato]

> It's not philosophically any different.

I disagree. I think using techniques that work by attempting to model physical processes that we understand are philosophically different from ML approaches that are learning arbitrary functions.

[shoo]

I agree there is a gulf of difference between modelling based on physics and mere empirical modelling fitting functions to data

[Reelin]

I agree with your earlier point, but disputing the usage of the term computational filtering here is truly pedantic. Yes, by definition machine learning approaches are a subset of computational approaches, but there are clear differences in terms of (at least) failure modes between machine learning and other techniques. In context, "non-machine-learning based filtering methods" is what was being referred to.

Importantly, the internals of non-machine-learning based approaches are more readily understandable and their output is much more predictable.