To help clinicians provide timely treatment and delay disease progress, it is crucial to identify
dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early
and late MCI stages before they progress to alzheimer's disease (AD). In the process of diagnosing MCI and
AD in living patients, brain scans are regularly collected using neuroimaging technologies such as computed
tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain
scans measure the volume and molecular activity within the brain resulting in a very promising avenue to
diagnose patients early in a non-invasive manner.
We have developed an optimal transport based transfer learning model to discriminate between
early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome
the over-tting problem and improve model stability and prediction accuracy.
With the transfer learning methods that we have developed, we outperform the current state of the
art MCI stage classication frameworks and show that it is crucial to leverage alzheimer's disease and normal
control subjects to accurately predict early and late stage cognitive impairment.
Our method is the current state of the art based on benchmark comparisons. This method is a
necessary technological stepping stone to widespread clinical usage of MRI based early detection of AD.