Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography

• URL: http://arxiv.org/abs/2204.07253v1
• Date: Thu, 14 Apr 2022 22:21:30 GMT
• Title: Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
• Authors: Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz, and Moncef Gabbouj
• Abstract summary: Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. One-class classification techniques are used to train a model for detecting a specific target class. The multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.
• Score: 22.479667537086108
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.

Related papers

• Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification [14.469786240272365]
  Early detection of myocardial infarction (MI) is vital to prevent further damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our proposed multi-view approach achieves a geometric mean of 71.24%, signifying a substantial advancement in echocardiography-based MI diagnosis.
  arXiv  Detail & Related papers  (2024-02-09T16:41:50Z)
• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [45.32169712547367]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)
• Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography [15.153823114115307]
  Early detection and localization of myocardial infarction can reduce the severity of cardiac damage. Deep learning techniques have shown promise for detecting MI in echocardiographic images. Our study introduces a robust method that combines features from multiple segmentation models to improve MI classification performance.
  arXiv  Detail & Related papers  (2023-03-12T20:16:14Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
  This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
  arXiv  Detail & Related papers  (2022-01-10T06:37:23Z)
• Early Myocardial Infarction Detection over Multi-view Echocardiography [22.81766862528411]
  Myocardial infarction (MI) is the leading cause of mortality in the world. In this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 11 myocardial segments can be analyzed for MI detection.
  arXiv  Detail & Related papers  (2021-11-09T15:36:10Z)
• Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification [63.44396343014749]
  We propose a new margin-based surrogate loss function for the AUC score. It is more robust than the commonly used. square loss while enjoying the same advantage in terms of large-scale optimization. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.
  arXiv  Detail & Related papers  (2020-12-06T03:41:51Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation [3.3426603061273994]
  We propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias.
  arXiv  Detail & Related papers  (2020-05-14T12:11:41Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification [0.0]
  Cardiac arrhythmia is a prevalent and significant cause of mortality and morbidity among cardiac ailments. Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model.
  arXiv  Detail & Related papers  (2020-04-11T13:24:17Z)

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.