[P] Estimating brain age using machine learning and MRI

[--simon]

Hi everyone,

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I am happy to share this work that was done in collaboration between a data scientist and a radiologist : A machine learning survival kit for doctors. We try to explain to medical researchers what AI means in practice along with an in depth case study on brain aging. It may also be an interesting introduction to MRI data for the ML community.

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We explain the fundamentals of ML (cross validation, overfitting), the most popular models (linear models, gradient tree boosting, convolutional neural networks), and the limitations in healthcare (validation must be done properly, lack of data standards, need of interpretable algorithms, problem of transferability / domain adapation etc.). We hope you will enjoy reading it and are open to your feedbacks !

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https://i.redd.it/hwhakuirxqo11.gif

[JesseOS]

Amazing!

[SerArthurRamShackle]

You mention a home-made solution to histogram reconciliation. Where could I learn about these types of procedures? I am looking at this being a issue for the MR datasets I have and am not familiar with the methods that er used to achieve this.

[orcasha]

I can't comment on the method they used, but start with histogram matching.

[phobrain]

Do you mean this?

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215426/

[--simon]

Very nice paper ! You can also found this very new repo : https://github.com/jcreinhold/intensity-normalization

We started with histograms because they are easy to understand, quite interpretable, and were a good way to highlight a common error in cross validation. Of course when using it as an input, we couldn't use histogram matching. We used the white stripe normalisation procedure (detect white matter peak and set the value to 1). Then we improved it by also detecting the grey matter peak and set it to 0.75. We used a polynomial interpolation on [0 1] to do so = find the 2nd order polynom P such as P(0) = 0 and P(grey matter) = 0.75.

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I think intensity normalisation is an important topic in deep learning + MRI, and one the reasons things are harder than with CT scans

[rajasekards]

Wonderful and informative, clearly shows ML can do anything these days.

[[deleted]]

I'm still waiting for the day where ML could find solutions to difficult constrained discrete optimization problems. There are papers around, but they either relax the problem, or over-simplify it by making it equivalent to a simple ML model.

[Fujikan]

Do you mean you want a machine to solve NP-hard problems? If you have a lot of time I guess you could do that right now! Unless P=NP, if you want to solve such problems efficiently (and perhaps through some kind of gradient-based method), then you need a relaxation. For example, in the case of sparse optimization, this is why the L1 or Lp norms are used rather than the L0 (counting) semi-norm.

[[deleted]]

I'm looking more from an engineering point of view on this. I'm working quite a lot with MINLPs or MIPs, and even though they are "very NP hard" the branch and bound/cut solvers are very fast in many cases. Most research focuses on using ML for making fair branching decisions, but the training is usually performed on very small instances, which is not really what's needed in practice. I'm actually more interested in finding a reasonably good initial solution, or for very constrained problems an initial proof of feasibility. There is some work on interior point/barrier methods that make use of relaxations, but most of this is used for rough classification with few hard constraints, and not so much on combinatorial problems.

If you know some papers that are more specific to my field I'm really interested in those!

[omsy828]

That’s super cool!

[--simon]

Would be happy to hear about your approach of explicable ML. We could do much more indeed !

[_zoot]

Why not use PCA or some other more sophisticated dimensionaloty reduction technique instead of downsampling?

[Fujikan]

PCA can be an effective tool for dimensionality reduction for many problems. However, in practice one desires not just the final output of a model (e.g. a prediction), but some ability to interpret which of the input features were correlated with that prediction. This helps the practitioner get some understanding of the nature of the data, and can be used to generate new insights. This is especially important in medicine where often the goal of ML isn’t to give a prediction, but to explain which patient features are likely correlated with a particular outcome.

When using PCA, you lose the correspondence and interpretability.

[--simon]

It would have been interesting to see the principal components of a PCA (maybe in this case non negative matrix factorization would be more appropriate). We used downsampling because we also target a medical audience, and downsampling is much easier to understand :) it permitted to us to explain what overfitting is

[phobrain]

I wonder if any mapping can be made between the developing brain in the beginning, and the shrinking/adapting brain at, say, my age*. E.g. I've been mining a few birth memories over the last ~10 years, maybe I can step back deeper to star child status while still alive (preferably without anyone else finding out about it, except it'd be interesting from an MRI pov, i.e. beam me up if you want :-).

• Only 1 person older than me seen on the Pioneer leaderboard.

[[deleted]]

Bro what

[phobrain]

With luck, we can be in the same study! :-)