IRAM-NET 模型:基于元学习的图像残留敏捷网络,用于罕见的新发胶质母细胞瘤诊断

Kuljeet Singh, Deepti Malhotra
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引用次数: 0

摘要

近年来,神经影像学和深度学习在诊断中枢神经系统 IV 级肿瘤新发胶质母细胞瘤方面受到了科学界的广泛关注。然而,用于训练的神经影像数据量稀少,导致许多深度学习模型存在严重的过拟合问题。为了应对这些挑战,我们提出了一种基于元学习的 IRAM-NET 模型,该模型利用 ResNet-50 作为基于深度学习的模型,并结合了元学习中的 e-MAML 集合技术,用于胶质母细胞瘤的早期诊断。所开发的方法利用从众多国家和国际癌症倡议数据存储库中获取的脑磁共振成像图像进行了训练和验证。在训练阶段,这项研究采用了详细的程序,包括处理异常和应用归一化技术。这些措施的实施保证了数据的精确表达,降低了过度拟合的风险,并增强了所提出模型的归纳能力。所提出的 IRAM-NET 模型在准确预测胶质母细胞瘤诊断方面超越了最新的研究,其训练、测试和验证准确率分别达到 97.22%、96.10% 和 94.74%。总体而言,这项研究不仅提高了胶质母细胞瘤等罕见疾病的诊断水平,还推动了元学习在医疗保健领域的广泛应用。这强调了在数据可用性有限的情况下,适应性和效率的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

IRAM–NET model: image residual agnostics meta-learning-based network for rare de novo glioblastoma diagnosis

IRAM–NET model: image residual agnostics meta-learning-based network for rare de novo glioblastoma diagnosis

In the recent years, neuroimaging and deep learning have received notable scientific attention for the diagnosis of grade IV tumor de novo glioblastoma in the central nervous system. However, the scarce amount of neuroimaging data for training has resulted in significant overfitting issues for numerous deep learning models. To address these challenges, we propose the implementation of a meta-learning-based IRAM–NET model that utilizes the ResNet-50 as a deep learning-based model and incorporates the e-MAML ensemble technique from meta-learning for the early diagnosis of glioblastoma. The methodology developed was trained and validated using brain MRI images taken from numerous national and international cancer initiative data repositories. In the training phase, this study employed detailed procedures, including the handling of exceptions and the application of normalization techniques. These measures were implemented to guarantee precise data representation, mitigate the risk of overfitting, and enhance the proposed model’s capacity for making meaningful generalizations. The proposed IRAM–NET model surpasses the most recent studies in accurately predicting glioblastoma diagnosis, achieving a training, testing and validation accuracy of 97.22%, 96.10%, and 94.74%, respectively. Overall, the research not only enhances the diagnosis of rare disorders like glioblastoma, but also promotes the wider inclusion of meta-learning in healthcare. This underlines the importance of adaptation and efficiency in situations with limited data availability.

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