Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia

• URL: http://arxiv.org/abs/2206.07417v1
• Date: Wed, 15 Jun 2022 09:44:30 GMT
• Title: Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia
• Authors: Huy-Dung Nguyen, Micha\"el Cl\'ement, Boris Mansencal, Pierrick Coup\'e
• Abstract summary: Alzheimer's disease and Frontotemporal dementia are two major types of dementia. differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease and Frontotemporal dementia are two major types of dementia. Their accurate diagnosis and differentiation is crucial for determining specific intervention and treatment. However, differential diagnosis of these two types of dementia remains difficult at the early stage of disease due to similar patterns of clinical symptoms. Therefore, the automatic classification of multiple types of dementia has an important clinical value. So far, this challenge has not been actively explored. Recent development of deep learning in the field of medical image has demonstrated high performance for various classification tasks. In this paper, we propose to take advantage of two types of biomarkers: structure grading and structure atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets to locally discriminate healthy versus dementia anatomical patterns. The result of these models is an interpretable 3D grading map capable of indicating abnormal brain regions. This map can also be exploited in various classification tasks using graph convolutional neural network. Finally, we propose to combine deep grading and atrophy-based classifications to improve dementia type discrimination. The proposed framework showed competitive performance compared to state-of-the-art methods for different tasks of disease detection and differential diagnosis.

Related papers

• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• 3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia [0.0]
  Alzheimer's disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms. We present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia.
  arXiv  Detail & Related papers  (2023-09-06T17:42:18Z)
• Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia [0.0]
  Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. We propose a deep learning based approach for both problems of disease detection and differential diagnosis.
  arXiv  Detail & Related papers  (2022-11-25T13:25:18Z)
• Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence [0.0]
  Deep learning methods have been proposed to automate diagnosis and prognosis of Alzheimer's disease. These methods often suffer from a lack of interpretability and generalization. We propose a novel deep framework designed to overcome these limitations.
  arXiv  Detail & Related papers  (2022-06-07T13:02:53Z)
• Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis [58.720142291102135]
  The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The use of information about outlier and Fractal Dimension features improves the system performance.
  arXiv  Detail & Related papers  (2022-03-21T09:57:20Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network [0.0]
  Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other. Differentiating between the two dementia types is crucial for determining disease-specific intervention and treatment. Recent development of Deep-learning-based approaches in the field of medical image computing are delivering some of the best performance for many binary classification tasks.
  arXiv  Detail & Related papers  (2021-09-12T22:40:50Z)
• An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization [3.9281410693767036]
  We propose an end-to-end deep learning approach for automated diagnosis of Alzheimer's disease (AD) and localization of important brain regions related to the disease from sMRI data. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI) The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and 3D CNN methods.
  arXiv  Detail & Related papers  (2021-07-28T07:19:00Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)
• 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.