A Robust Deep Model for Improved Classification of AD/MCI Patients

Accurate classification of Alzheimer\u27s disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods

Towards AI-Assisted Disease Diagnosis: Learning Deep Feature Representations for Medical Image Analysis

Artificial Intelligence (AI) has impacted our lives in many meaningful ways. For our research, we focus on improving disease diagnosis systems by analyzing medical images using AI, specifically deep learning technologies. The recent advances in deep learning technologies are leading to enhanced performance for medical image analysis and computer-aided disease diagnosis. In this dissertation, we explore a major research area in medical image analysis - Image classification. Image classification is the process to assign an image a label from a fixed set of categories. For our research, we focus on the problem of Alzheimer\u27s Disease (AD) diagnosis from 3D structural Magnetic Resonance Imaging (sMRI) and Positron Emission Tomography (PET) brain scans. Alzheimer\u27s Disease is a severe neurological disorder. In this dissertation, we address challenges related to Alzheimer\u27s Disease diagnosis and propose several models for improved diagnosis. We focus on analyzing the 3D Structural MRI (sMRI) and Positron Emission Tomography (PET) brain scans to identify the current stage of Alzheimer\u27s Disease: Normal Controls (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). This dissertation demonstrates ways to improve the performance of a Convolutional Neural Network (CNN) for Alzheimer\u27s Disease diagnosis. Besides, we present approaches to solve the class-imbalance problem and improving classification performance with limited training data for medical image analysis. To understand the decision of the CNN, we present methods to visualize the behavior of a CNN model for disease diagnosis. As a case study, we analyzed brain PET scans of AD and CN patients to see how CNN discriminates among data samples of different classes. Additionally, this dissertation proposes a novel approach to generate synthetic medical images using Generative Adversarial Networks (GANs). Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. Our proposed model can solve such issue and generate brain MRI and PET images for three different stages of Alzheimer\u27s Disease - Normal Control (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). Our proposed approach can be generalized to create synthetic data for other medical image analysis problems and help to develop better disease diagnosis model