A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage

• URL: http://arxiv.org/abs/2408.00940v1
• Date: Thu, 1 Aug 2024 22:08:52 GMT
• Title: A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage
• Authors: Caiwen Jiang, Tianyu Wang, Xiaodan Xing, Mianxin Liu, Guang Yang, Zhongxiang Ding, Dinggang Shen,
• Abstract summary: We propose a novel prediction framework for measuring postoperative cerebral hemorrhage using only the patient's initial CT scan. Our method can generate follow-up CT scans better than state-of-the-art methods, and achieves an accuracy of 86.37% in predicting follow-up prognostic labels.
• Score: 42.24368372333753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ischemic stroke is a severe condition caused by the blockage of brain blood vessels, and can lead to the death of brain tissue due to oxygen deprivation. Thrombectomy has become a common treatment choice for ischemic stroke due to its immediate effectiveness. But, it carries the risk of postoperative cerebral hemorrhage. Clinically, multiple CT scans within 0-72 hours post-surgery are used to monitor for hemorrhage. However, this approach exposes radiation dose to patients, and may delay the detection of cerebral hemorrhage. To address this dilemma, we propose a novel prediction framework for measuring postoperative cerebral hemorrhage using only the patient's initial CT scan. Specifically, we introduce a dual-task mutual learning framework to takes the initial CT scan as input and simultaneously estimates both the follow-up CT scan and prognostic label to predict the occurrence of postoperative cerebral hemorrhage. Our proposed framework incorporates two attention mechanisms, i.e., self-attention and interactive attention. Specifically, the self-attention mechanism allows the model to focus more on high-density areas in the image, which are critical for diagnosis (i.e., potential hemorrhage areas). The interactive attention mechanism further models the dependencies between the interrelated generation and classification tasks, enabling both tasks to perform better than the case when conducted individually. Validated on clinical data, our method can generate follow-up CT scans better than state-of-the-art methods, and achieves an accuracy of 86.37% in predicting follow-up prognostic labels. Thus, our work thus contributes to the timely screening of post-thrombectomy cerebral hemorrhage, and could significantly reform the clinical process of thrombectomy and other similar operations related to stroke.

Related papers

• Machine learning for cerebral blood vessels' malformations [38.524104108347764]
  Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. Parameters of cerebral blood flow could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis.
  arXiv  Detail & Related papers  (2024-11-25T12:58:00Z)
• Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network [8.80381582892208]
  We propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. Our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages =4.5 hours compared to 0.858 using a conventional approach.
  arXiv  Detail & Related papers  (2023-06-21T13:00:49Z)
• Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models [4.780704816027884]
  We proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. Our top model achieved an average cross-validated ROC-AUC of 77.33 $pm$ 3.9%.
  arXiv  Detail & Related papers  (2023-02-08T15:41:21Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement [1.3814679165245243]
  A stroke occurs when an artery in the brain ruptures and bleeds or when the blood supply to the brain is cut off. This study proposes a deep learning-based method to score the CT scan for ASPECTS.
  arXiv  Detail & Related papers  (2022-03-05T06:12:49Z)
• 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)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo [55.04215695343928]
  The aortic vessel tree is composed of the aorta and its branching arteries. We systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree.
  arXiv  Detail & Related papers  (2021-08-06T08:18:28Z)
• DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging [26.162788846435365]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. We propose a novel deep neural network for the survival prediction of resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network(CE-ConvLSTM) We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.
  arXiv  Detail & Related papers  (2020-08-26T22:51:24Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)

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.