Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)

• URL: http://arxiv.org/abs/2211.09068v1
• Date: Wed, 16 Nov 2022 17:32:29 GMT
• Title: Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)
• Authors: Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulanger, Brian Buck
• Abstract summary: Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a blocked artery. Current standard AIS assessment method, which thresholds the 3D measurement maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate enough. We propose imbalanced Temporal Deep Process (iTDGP), a probabilistic model that can improve AIS prediction by using baseline Gaussian time series.
• Score: 2.649401887836554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As one of the leading causes of mortality and disability worldwide, Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a blocked artery. Within seconds of AIS onset, the brain cells surrounding the blocked artery die, which leads to the progression of the lesion. The automated and precise prediction of the existing lesion plays a vital role in the AIS treatment planning and prevention of further injuries. The current standard AIS assessment method, which thresholds the 3D measurement maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate enough. Due to this fact, in this article, we propose the imbalanced Temporal Deep Gaussian Process (iTDGP), a probabilistic model that can improve AIS lesions prediction by using baseline CTP time series. Our proposed model can effectively extract temporal information from the CTP time series and map it to the class labels of the brain's voxels. In addition, by using batch training and voxel-level analysis iTDGP can learn from a few patients and it is robust against imbalanced classes. Moreover, our model incorporates a post-processor capable of improving prediction accuracy using spatial information. Our comprehensive experiments, on the ISLES 2018 and the University of Alberta Hospital (UAH) datasets, show that iTDGP performs better than state-of-the-art AIS lesion predictors, obtaining the (cross-validation) Dice score of 71.42% and 65.37% with a significant p<0.05, respectively.

Related papers

• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series [2.621671379723151]
  We use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model. We train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
  arXiv  Detail & Related papers  (2023-11-16T05:06:51Z)
• 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)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury [12.651451007914124]
  This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes.
  arXiv  Detail & Related papers  (2022-08-08T13:18:35Z)
• Domain Knowledge Driven 3D Dose Prediction Using Moment-Based Loss Function [3.2653790770825686]
  Dose volume histogram (DVH) metrics are widely accepted evaluation criteria in the clinic. We propose a novel moment-based loss function for predicting 3D dose distribution.
  arXiv  Detail & Related papers  (2022-07-07T16:35:06Z)
• An End-to-End Deep Learning Approach for Epileptic Seizure Prediction [4.094649684498489]
  We propose an end-to-end deep learning solution using a convolutional neural network (CNN) Overall sensitivity, false prediction rate, and area under receiver operating characteristic curve reaches 93.5%, 0.063/h, 0.981 and 98.8%, 0.074/h, 0.988 on two datasets respectively.
  arXiv  Detail & Related papers  (2021-08-17T05:49:43Z)
• AutoPtosis [0.368986335765876]
  AutoPtosis is an artificial intelligence based system with interpretable results for rapid diagnosis of ptosis. The proposed algorithm can help in the rapid and timely diagnosis of ptosis, significantly reduce the burden on the healthcare system, and save the patients and clinics valuable resources.
  arXiv  Detail & Related papers  (2021-06-07T18:32:31Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.