CASCADE-FSL: Few-shot learning for collateral evaluation in ischemic stroke

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Mumu Aktar, Donatella Tampieri, Yiming Xiao, Hassan Rivaz, Marta Kersten-Oertel
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引用次数: 0

Abstract

Assessing collateral circulation is essential in determining the best treatment for ischemic stroke patients as good collaterals lead to different treatment options, i.e., thrombectomy, whereas poor collaterals can adversely affect the treatment by leading to excess bleeding and eventually death. To reduce inter- and intra-rater variability and save time in radiologist assessments, computer-aided methods, mainly using deep neural networks, have gained popularity. The current literature demonstrates effectiveness when using balanced and extensive datasets in deep learning; however, such data sets are scarce for stroke, and the number of data samples for poor collateral cases is often limited compared to those for good collaterals. We propose a novel approach called CASCADE-FSL to distinguish poor collaterals effectively. Using a small, unbalanced data set, we employ a few-shot learning approach for training using a 2D ResNet-50 as a backbone and designating good and intermediate cases as two normal classes. We identify poor collaterals as anomalies in comparison to the normal classes. Our novel approach achieves an overall accuracy, sensitivity, and specificity of 0.88, 0.88, and 0.89, respectively, demonstrating its effectiveness in addressing the imbalanced dataset challenge and accurately identifying poor collateral circulation cases.
CASCADE-FSL:用于缺血性脑卒中侧支评估的快速学习方法
评估侧支循环对于确定缺血性卒中患者的最佳治疗方法至关重要,因为良好的侧支可导致不同的治疗选择,例如取栓,而不良的侧支可导致过量出血并最终死亡,从而对治疗产生不利影响。为了减少放射科医生评估的内部和内部差异,节省时间,主要使用深度神经网络的计算机辅助方法得到了普及。目前的文献表明,在深度学习中使用平衡和广泛的数据集是有效的;然而,这些数据集对于中风来说是稀缺的,并且与良好的抵押品相比,不良抵押品案例的数据样本数量通常是有限的。我们提出了一种称为CASCADE-FSL的新方法来有效区分不良抵押品。使用一个小的,不平衡的数据集,我们采用少量的学习方法进行训练,使用2D ResNet-50作为主干,并将良好和中等情况指定为两个正常类。我们将不良抵押品视为与正常类别相比的异常。我们的新方法的总体准确性、灵敏度和特异性分别为0.88、0.88和0.89,证明了其在解决数据不平衡挑战和准确识别不良侧支循环病例方面的有效性。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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