Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment

• URL: http://arxiv.org/abs/2008.00748v1
• Date: Mon, 3 Aug 2020 10:04:09 GMT
• Title: Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
• Authors: Wen Yu, Baiying Lei, Michael K.Ng, Albert C.Cheung, Yanyan Shen, Shuqiang Wang
• Abstract summary: A novel tensorizing GAN with high-order pooling is proposed to assess Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) To the best of our knowledge, the proposed-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis.
• Score: 38.936005220639316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is of great significance to apply deep learning for the early diagnosis of Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess Mild Cognitive Impairment (MCI) and AD. By tensorizing a three-player cooperative game based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of the holistic Magnetic Resonance Imaging (MRI) images. To the best of our knowledge, the proposed Tensor-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semi-supervised learning purpose.

Related papers

• Generative forecasting of brain activity enhances Alzheimer's classification and interpretation [16.09844316281377]
  Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor neural activity. Deep learning has shown promise in capturing these representations. In this study, we focus on time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation.
  arXiv  Detail & Related papers  (2024-10-30T23:51:31Z)
• An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease [4.304406827494684]
  We propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features.
  arXiv  Detail & Related papers  (2023-08-15T13:54:50Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• 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)
• Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN [40.50404819220093]
  A novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of Alzheimer's disease (AD) MP-GAN achieves superior performance compared with the existing methods.
  arXiv  Detail & Related papers  (2021-11-25T03:24:52Z)
• Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities [60.079782224958414]
  We will present and discuss opportunities and challenges brought by a new deep learning method by AUC (aka underlinebf Deep underlinebf AUC classification)
  arXiv  Detail & Related papers  (2021-11-01T15:31:32Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI [22.34325971680329]
  We have proposed a novel computer-aided approach for early diagnosis of Alzheimer's disease by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. The experimental results show that the proposed approach has a competitive advantage over the state-of-the-art models in terms of accuracy performance and generalizability.
  arXiv  Detail & Related papers  (2020-08-10T11:08:55Z)

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.