I am trying to create a model that can predict Type 2 diabetes in a patient based on MRI scans of their thigh muscle. Previous literature has shown that fat deposition in the muscle of femur is linked to Type 2 Diabetes, so there is some valid relationship here.

I have a dataset comprised of several hundred patients. I am analyzing radiomics features of their MRI scans, which are basically quantitative imaging features (think things like texture, intensity, variance of texture in a specific direction, etc.). The kicker here is that an MRI scan is a three-dimensional object, but I have radiomics features for each of the 2D slices, not radiomics of the entire 3D thigh muscle. So this dataset has repeated rows for each patients, "multiple records for one observation." My objective is to output a binary classification of Yes/No for T2DM for a single patient.

Problem Description

Based on some initial exploratory data analysis, I think the key here is that some slices are more informative than others. For example, one thing I tried was to group the slices by patient, and then analyze each slice in feature hyperspace. I selected the slice with the furthest distance from the center of all the other slices in feature hyperspace and used only that slice for the patient.

I have also tried just aggregating all the features, so that each patient is reduced to a single row, but has way more features. For example, there might be a feature called "median intensity." But now the patient will have 5 features, called "median intensity__mean" "median intensity__median", "median intensity__max," and so forth. These aggregations are across all the slices that belong to that patient. This did not work well and yielded an AUC of 0.5.

I'm trying to find a way to select the most informative slices for each patient that will then be used for the classification; or an informative way of reducing all the records for a single observation down to a single record.

Solution Thoughts

One thing I'm thinking is that it would probably be best to train some sort of neural net to learn which slices to pick before feeding those slices to another classifier. Effectively, how this would work would be the neural net would learn a linear transformation that could be applied to the matrix of (slices, features) for each patient. So some slices would be upweighted while others would be downweighted. Then I could compute the mean along the ith axis and then use that as input to the final classifier. If you have examples of code for how this would work (I'm not sure how you would hook up the loss function from the final classifier (in my case, a LGBMClassifier) to the neural net so that backpropagation occurs from the final classification all throughout the ensemble model.

Overall, I'm open to any ideas on how to approach this issue of reducing multiple records for one observation down to the most informative / subset of the most informative records for one observation.



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