A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

• URL: http://arxiv.org/abs/2208.08848v1
• Date: Thu, 18 Aug 2022 14:32:16 GMT
• Title: A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction
• Authors: Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung, and Hubert P. H. Shum
• Abstract summary: Musculoskeletal and neurological disorders are the most common causes of walking problems among older people. Recent deep learning-based methods have shown promising results for automated analysis.
• Score: 14.003588854239544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

Related papers

• DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis [20.178933135186618]
  We propose a dual-stream pipeline that facilitates cross-task and cross-architecture knowledge sharing. Our approach introduces a dual-stream embedding module that unifies feature representations from segmentation and classification models. We validated our method on multiple 3D datasets for Alzheimer's disease diagnosis, demonstrating significant improvements in classification performance.
  arXiv  Detail & Related papers  (2024-09-11T19:31:01Z)
• Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data [54.55126643084341]
  Existing deep learning solutions suffer from three major limitations. We introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets.
  arXiv  Detail & Related papers  (2024-01-01T10:20:01Z)
• Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps [1.5501208213584152]
  Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. We propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests. To tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD)
  arXiv  Detail & Related papers  (2023-10-25T19:02:57Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Multimodal Gait Recognition for Neurodegenerative Diseases [38.06704951209703]
  We propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases. A new correlative memory neural network architecture is designed for extracting temporal features. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
  arXiv  Detail & Related papers  (2021-01-07T10:17:11Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency [40.67137486295487]
  We propose a novel approach for knee cartilage defects assessment, including severity classification and lesion localization. A self-ensembling framework is composed of a student network and a teacher network with the same structure. Experiments show that the proposed method can significantly improve the self-ensembling performance in both knee cartilage defects classification and localization.
  arXiv  Detail & Related papers  (2020-05-19T04:47:25Z)

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.