Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network (1607.00556v1)

Abstract: Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the \emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy and robustness. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the \emph{CADDementia} dataset.

Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

The paper presents an advanced framework for Alzheimer's Disease (AD) diagnosis using a Deeply Supervised Adaptable 3D Convolutional Network (DSA-3D-CNN). Alzheimer's Disease, a widely prevalent cause of dementia, necessitates early and accurate detection methods to facilitate intervention and slow disease progression. This research focuses on leveraging deep learning techniques to enhance the precision and adaptability of AD diagnostics using structural brain MRI scans.

The authors' approach integrates a 3D Convolutional Autoencoder (3D-CAE) for unsupervised training, capturing variations in anatomical features related to AD. These pre-trained lower layers of the network serve to identify generic biomarkers of Alzheimer’s, such as ventricular size and hippocampal shape. A key aspect of the methodology is domain adaptation, enabling the model to generalize learned features from a source dataset (CADDementia) and apply them effectively to a target dataset (ADNI), thereby minimizing data distribution discrepancies.

A pivotal contribution of this framework is the emphasis on transferring learned features across domains, coupled with the execution of task-specific fine-tuning. The deeply supervised mechanism incorporated into the network enhances the discrimination ability of the model, improving classification accuracy. The authors deploy a series of fully connected layers that undergo deep supervision, ensuring the network's robustness against dataset biases. This technique markedly improves the predictive performance compared to single-task learning paradigms.

The experimental evaluations reinforce the efficacy of the proposed model. The DSA-3D-CNN demonstrates superior classification accuracy across various tasks, including binary and ternary classification challenges on the ADNI dataset. Notably, for the AD vs. NC classification task, the model achieves a peak accuracy of 99.3%, indicating significant advancements over conventional classifiers that employ multiple imaging modalities. The area under the ROC curve (AUC) also highlights the strengths of the presented approach, confirming its reliability and robustness.

From a practical standpoint, this system offers substantial improvements in accurately identifying AD patients at an earlier stage, which can contribute to better management and treatment strategies, potentially alleviating socio-economic burdens associated with the disease. Theoretically, the model's ability to generalize and adapt to new datasets suggests promising directions for research in medical diagnostics using deep learning. These insights pave the way for future endeavors in the field of artificial intelligence, particularly in the application of convolutional networks to complex medical data scenarios.

In conclusion, this paper adds a significant layer of improvement to AD diagnostics through deep learning methodologies. The adaptable nature of the DSA-3D-CNN system, combined with its deeply supervised architecture, serves as a powerful tool in medical imaging and diagnostics. It stands as an exemplar for the application of transfer learning and domain adaptation in deep neural networks, potentially influencing future developments in AI-based medical technologies.