Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification

• URL: http://arxiv.org/abs/2304.13775v2
• Date: Fri, 5 Jan 2024 16:13:31 GMT
• Title: Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification
• Authors: Koushik Sivarama Krishnan, P. J. Joe Nikesh, Swathi Gnanasekar, Karthik Sivarama Krishnan
• Abstract summary: An innovative two-stage methodology for categorizing blood clot origins is presented in this paper. A background classifier based on MobileNetV3 segments big whole-slide digital pathology images into numerous tiles to detect the presence of cellular material. Different pre-trained image classification algorithms are fine-tuned to determine the origin of blood clots.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An innovative two-stage methodology for categorizing blood clot origins is presented in this paper, which is important for the diagnosis and treatment of ischemic stroke. First, a background classifier based on MobileNetV3 segments big whole-slide digital pathology images into numerous tiles to detect the presence of cellular material. After that, different pre-trained image classification algorithms are fine-tuned to determine the origin of blood clots. Due to complex blood flow dynamics and limitations in conventional imaging methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound, identifying the sources of blood clots is a challenging task. Although these techniques are useful for identifying blood clots, they are not very good at determining how they originated. To address these challenges, our method makes use of robust computer vision models that have been refined using information from whole-slide digital pathology images. Out of all the models tested, the PoolFormer \cite{yu2022metaformer} performs better than the others, with 93.4\% accuracy, 93.4\% precision, 93.4\% recall, and 93.4\% F1-score. Moreover, it achieves the good weighted multi-class logarithmic loss (WMCLL) of 0.4361, which emphasizes how effective it is in this particular application. These encouraging findings suggest that our approach can successfully identify the origin of blood clots in a variety of vascular locations, potentially advancing ischemic stroke diagnosis and treatment approaches.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin [0.0]
  Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin.
  arXiv  Detail & Related papers  (2024-05-01T23:40:12Z)
• TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning [0.08376229126363229]
  We propose a deep learning model to segment the vascular system in images of patients undergoing surgery for peripheral arterial disease (PAD) Our study focused on accurately segmenting the vascular system (1) from the descending thoracic aorta to the iliac bifurcation and (2) from the descending thoracic aorta to the knees in CTA images using deep learning techniques.
  arXiv  Detail & Related papers  (2023-11-17T04:59:08Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images [1.1254693939127909]
  Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque.
  arXiv  Detail & Related papers  (2022-02-04T23:10:24Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• 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)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Grading the Severity of Arteriolosclerosis from Retinal Arterio-venous Crossing Patterns [27.867833878756553]
  The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. We propose a deep learning approach to support the diagnosis process, which is one of the earliest attempts in medical imaging.
  arXiv  Detail & Related papers  (2020-11-07T13:15:17Z)
• A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting [0.0]
  We present an anomaly detection method based on two trained flow-based generative models. With this method, the posterior probability can be computed as a normality metric for any given image. The method was validated with two types of medical images: chest X-ray radiographs (CXRs) and brain computed tomographies (BCTs)
  arXiv  Detail & Related papers  (2020-01-22T02:01:57Z)

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.