Experimental Setup & Preliminary Result
In a previous work we have shown that using a convolutional autoencoder we can extract diagnostically informative deep features from brain structural MRI . By Applying maximum mean discrepancy test to the deep features we were able to differentiate between Alzheimer’s disease, cognitive impairment and cognitively normal subjects, as well as carriers of the Alzheimer’s disease major genetic risk factor, Apolipoprotein epsilon 4 (APOE4).
While in the previouse work we have used T1 brain MRI scans and took an unsupervised approach, in this work, we have used ventricle segmentations as model input and trained convolutional autoencoder with an auxiliary regression task to predict the volume of the ventricles. We used generalized dice loss and mean squared error for reconstruction and regression, respectively. When calculating the total loss we multiplied the reconstruction loss by a factor of 10 to counteract task imbalance. The model was trained and validated on separate sets of 5000 subjects from UK Biobank and evaluated on 1200 subjects from the ADNI data set. We generated the deep feature for these ADNI subjects, calculated their first principle components and compared these features with the previouse features generated from the T1 whole brain MRI scans (Figure 1).
We were able to achieve higher accurcy in the two sample tests in comparison to our previouse experiments (Figure 2).
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