Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework

• URL: http://arxiv.org/abs/2106.04966v1
• Date: Wed, 9 Jun 2021 10:25:34 GMT
• Title: Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework
• Authors: Kevin D. McCay, Edmond S. L. Ho, Dimitrios Sakkos, Wai Lok Woo, Claire Marcroft, Patricia Dulson, Nicholas D. Embleton
• Abstract summary: We propose a new framework for the automated classification of infant body movements, based upon the General Movements Assessment (GMA) Our proposed framework segments extracted features to detect presence of Fidgety Movements (FMs) associated with the GMAtemporally. We quantitatively compare the proposed framework's classification performance with several other methods from the literature and qualitatively evaluate the visualization's veracity.
• Score: 4.847839438526929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Providing early diagnosis of cerebral palsy (CP) is key to enhancing the developmental outcomes for those affected. Diagnostic tools such as the General Movements Assessment (GMA), have produced promising results in early diagnosis, however these manual methods can be laborious. In this paper, we propose a new framework for the automated classification of infant body movements, based upon the GMA, which unlike previous methods, also incorporates a visualization framework to aid with interpretability. Our proposed framework segments extracted features to detect the presence of Fidgety Movements (FMs) associated with the GMA spatiotemporally. These features are then used to identify the body-parts with the greatest contribution towards a classification decision and highlight the related body-part segment providing visual feedback to the user. We quantitatively compare the proposed framework's classification performance with several other methods from the literature and qualitatively evaluate the visualization's veracity. Our experimental results show that the proposed method performs more robustly than comparable techniques in this setting whilst simultaneously providing relevant visual interpretability.

Related papers

• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Multi-Modal Evaluation Approach for Medical Image Segmentation [4.989480853499916]
  We propose a novel multi-modal evaluation (MME) approach to measure the effectiveness of different segmentation methods. We introduce new relevant and interpretable characteristics, including detection property, boundary alignment, uniformity, total volume, and relative volume. Our proposed approach is open-source and publicly available for use.
  arXiv  Detail & Related papers  (2023-02-08T15:31:33Z)
• 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)
• Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment [11.33138866472943]
  General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA. We propose a semi-supervised model, termed SiamParseNet (SPN), which consists of two branches, one for intra-frame body parts segmentation and another for inter-frame label propagation.
  arXiv  Detail & Related papers  (2022-10-14T18:46:30Z)
• Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network [5.541644538483947]
  The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. Recent video-based GMs classification has attracted attention, but this approach would be strongly affected by irrelevant information. We propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information.
  arXiv  Detail & Related papers  (2022-07-04T05:21:09Z)
• Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification [0.0]
  We propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography. We create a medical diagnostic attribute dataset to improve disease classification using OCT.
  arXiv  Detail & Related papers  (2022-03-20T18:37:20Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection [49.14070210387509]
  We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection. AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-21T06:48:34Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation [33.684182783291064]
  Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. The proposed framework consists of two models which provide reliable guidance for each other. Experiments on the publicly available AGE dataset demonstrate that the proposed framework outperforms the state-of-the-art semi-/weakly-supervised methods.
  arXiv  Detail & Related papers  (2020-07-20T11:26:32Z)

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.