Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia

• URL: http://arxiv.org/abs/2211.14096v2
• Date: Mon, 11 Sep 2023 16:44:52 GMT
• Title: Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia
• Authors: Huy-Dung Nguyen, Micha\"el Cl\'ement, Vincent Planche, Boris Mansencal, Pierrick Coup\'e
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases and their differential diagnosis is sometimes difficult for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinical practice. However, current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. In this paper, we propose a deep learning based approach for both problems of disease detection and differential diagnosis. We suggest utilizing two types of biomarkers for this application: structure grading and structure atrophy. First, we propose to train a large ensemble of 3D U-Nets to locally determine the anatomical patterns of healthy people, patients with Alzheimer's disease and patients with Frontotemporal dementia using structural MRI as input. The output of the ensemble is a 2-channel disease's coordinate map able to be transformed into a 3D grading map which is easy to interpret for clinicians. This 2-channel map is coupled with a multi-layer perceptron classifier for different classification tasks. Second, we propose to combine our deep learning framework with a traditional machine learning strategy based on volume to improve the model discriminative capacity and robustness. After both cross-validation and external validation, our experiments based on 3319 MRI demonstrated competitive results of our method compared to the state-of-the-art methods for both disease detection and differential diagnosis.

Related papers

• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• 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)
• Interpretable differential diagnosis for Alzheimer's disease and Frontotemporal dementia [0.0]
  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.
  arXiv  Detail & Related papers  (2022-06-15T09:44:30Z)
• 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)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• 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)
• 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.