GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration

• URL: http://arxiv.org/abs/2311.04772v1
• Date: Wed, 8 Nov 2023 15:51:12 GMT
• Title: GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration
• Authors: Xuhao Shan, Xinyang Li, Ruiquan Ge, Shibin Wu, Ahmed Elazab, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qingying Xiao, Xiang Wan, Changmiao Wang
• Abstract summary: Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged brain blood vessel ruptures. This paper introduces a novel deep learning algorithm, GCS-ICHNet, which integrates multimodal brain CT data and the Glasgow Coma Scale score to improve prognosis.
• Score: 19.51978172091416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged brain blood vessel ruptures, often leading to complications and fatalities. Timely and accurate prognosis and management are essential due to its high mortality rate. However, conventional methods heavily rely on subjective clinician expertise, which can lead to inaccurate diagnoses and delays in treatment. Artificial intelligence (AI) models have been explored to assist clinicians, but many prior studies focused on model modification without considering domain knowledge. This paper introduces a novel deep learning algorithm, GCS-ICHNet, which integrates multimodal brain CT image data and the Glasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizes a transformer-based fusion module for assessment. GCS-ICHNet demonstrates high sensitivity 81.03% and specificity 91.59%, outperforming average clinicians and other state-of-the-art methods.

Related papers

• Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models [5.817643726988823]
  This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes.
  arXiv  Detail & Related papers  (2024-07-23T04:18:34Z)
• Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification [0.0]
  Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. Histopathology remains the gold standard, although time-consuming and vulnerable to inter-observer mistakes. Recent advances in deep learning have generated interest in its application to medical imaging analysis.
  arXiv  Detail & Related papers  (2024-05-07T18:49:34Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Lesion detection in contrast enhanced spectral mammography [0.0]
  The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases.
  arXiv  Detail & Related papers  (2022-07-20T06:49:02Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients [31.567542945171834]
  We describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge. BraTS-Reg is the first public benchmark environment for deformable registration algorithms. The aim of BraTS-Reg is to continue to serve as an active resource for research.
  arXiv  Detail & Related papers  (2021-12-13T19:25:16Z)
• Quality control for more reliable integration of deep learning-based image segmentation into medical workflows [0.23609258021376836]
  We present an analysis of state-of-the-art automatic quality control (QC) approaches to estimate the certainty of their outputs. We validated the most promising approaches on a brain image segmentation task identifying white matter hyperintensities (WMH) in magnetic resonance imaging data.
  arXiv  Detail & Related papers  (2021-12-06T16:30:43Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

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.