A Hybrid Learning Pipeline for Automated Diagnosis of First-Episode Schizophrenia Utilizing T1-weighted Images.

Abstract

In this work, we proposed and validated a hybrid learning pipeline for automated diagnosis of first-episode schizophrenia (FES) utilizing T1-weighted images. Amygdalar and hippocampal shape abnormalities in FES have been observed in previous studies. In this work, we jointly made use of two types of features, together with advanced machine learning techniques, for an automated discrimination of FES and healthy control (96 versus 102). Specifically, we first employed a ResNet34 model to extract convolutional neural network (CNN) features. We then combined these CNN features with shape features of the bilateral hippocampi and the bilateral amygdalas, before being inputted to advanced classification algorithms such as the Gradient Boosting Decision Tree (GBDT) for classifying between FES and healthy control. Shape features were represented using log Jacobian determinants, through a well-established statistical shape analysis pipeline. When combining CNN with hippocampal shape, the best results came from utilizing GBDT as the classifier, with an overall accuracy of 75.15%, a sensitivity of 69.35%, a specificity of 80.19%, an F1 of 72.16%, and an AUC of 79.68%. When combing CNN and amygdalar shape, the best results came from utilizing Bagging as the classifier, with an overall accuracy of 74.39%, a sensitivity of 67.93%, a specificity of 80%, an F1 of 71.11%, and an AUC of 80.98%. Compared with using each single set of features, either CNN or shape, significant improvements have been observed, in terms of FES discrimination. To the best of our knowledge, this is the first work that has tried to combine CNN features and hippocampal/amygdalar shape features for automated FES identification.