Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

• URL: http://arxiv.org/abs/2008.06151v3
• Date: Thu, 20 Aug 2020 06:58:20 GMT
• Title: Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
• Authors: Emanuel A. Azcona, Pierre Besson, Yunan Wu, Arjun Punjabi, Adam Martersteck, Amil Dravid, Todd B. Parrish, S. Kathleen Bandt, Aggelos K. Katsaggelos
• Abstract summary: We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. We outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem.
• Score: 6.088308871328403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.

Related papers

• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition [59.28732531600606]
  We propose a framework named textbfClass textbfAttention to textbfREgions of the lesion (CARE) to handle data imbalance issues. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework.
  arXiv  Detail & Related papers  (2023-07-19T15:19:02Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease [0.0]
  This paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images. The proposed model achieved state-of-the-art performance in the Alzheimer's disease (AD) vs mild cognitive impairment (MCI) vs cognitively normal (CN) classification task with a 3% increase in accuracy from what is reported in the literature.
  arXiv  Detail & Related papers  (2023-01-31T09:44:52Z)
• A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging [17.775145204666874]
  We present an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. We find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.
  arXiv  Detail & Related papers  (2022-10-29T19:03:01Z)
• An explainability framework for cortical surface-based deep learning [110.83289076967895]
  We develop a framework for cortical surface-based deep learning. First, we adapted a perturbation-based approach for use with surface data. We show that our explainability framework is not only able to identify important features and their spatial location but that it is also reliable and valid.
  arXiv  Detail & Related papers  (2022-03-15T23:16:49Z)
• A deep learning model for data-driven discovery of functional connectivity [0.0]
  We propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. We demonstrate the model's state-of-the-art classification performance on a schizophrenia fMRI dataset.
  arXiv  Detail & Related papers  (2021-12-07T21:57:32Z)
• Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis [4.5484714814315685]
  BrainBagNet is a framework for jointly learning pathological region localization and Alzheimer's disease diagnosis. The proposed method represents the patch-level response from whole-brain MRI scans and discriminative brain-region from position information. In five-fold cross-validation, the classification performance of the proposed method outperformed that of the state-of-the-art methods in both AD diagnosis and mild cognitive impairment prediction tasks.
  arXiv  Detail & Related papers  (2021-08-10T10:06:54Z)
• Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models [16.218680291606628]
  Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders. We propose to train simulator networks that can warp a given image to inject or remove patterns of the disease. We apply our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of the Alzheimer's disease and alcohol use disorder.
  arXiv  Detail & Related papers  (2021-02-16T15:57:37Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.