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Abstract：
Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings.
摘要：
深度学习深刻影响了各个领域，特别是医学图像分析。该领域的传统迁移学习方法依赖于在特定领域的医学数据集上预训练的模型，这限制了它们的通用性和可访问性。在这项研究中,我们提出了一个叫做真实世界特征迁移学习的新框架，它利用最初在大规模通用数据集如ImageNet上训练的骨干模型。与从头开始训练的模型相比，我们评估了这种方法的有效性和鲁棒性，专注于对X射线图像中的肺炎进行分类的任务。我们的实验,其中包括将灰度图像转换为RGB格式，证明了真实世界的特征迁移学习在各种性能指标上始终优于传统的训练方法。这一进步有可能通过利用从通用预训练模型学习的丰富特征表示来加速医学成像中的深度学习应用。所提出的方法克服了特定领域预训练模型的局限性，从而加速医疗诊断和医疗保健领域的创新。从数学的角度来看,我们形式化现实世界的特征迁移学习的概念，并提供了一个严格的数学公式的问题。我们的实验结果提供了支持这种方法有效性的经验证据，为进一步的理论分析和探索奠定基础。这项工作有助于更广泛地理解跨域的特征可转移性，并对开发准确有效的医学图像分析模型具有重要意义。即使在资源受限的环境中。
