用SVD-LS方法实现高效肺炎分类，无需微调模型
SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features
- 基于SVD的闭式解分类，避免梯度更新
- 在X光片上达到与微调相当的准确率
- 适合资源受限的实时医疗诊断场景
通过X光影像早期准确诊断肺炎对治疗和患者预后至关重要。近年来机器学习进步使得自动化辅助诊断工具成为可能，帮助放射科医生做出更可靠、高效的决策。本文提出一种基于奇异值分解的最小二乘法（SVD-LS）框架，用于多类肺炎分类，利用最先进的自监督和迁移学习模型提取深度特征。相比计算开销大的梯度微调，该方法采用闭式、非迭代的分类策略，确保高效率且不损失准确性。实验表明，SVD-LS在保持竞争力性能的同时显著降低计算成本，适用于实时医学影像应用。代码已开源：github.com/meterdogan07/SVD-LS。
原文摘要 · Abstract (English)
Accurate and early diagnosis of pneumonia through X-ray imaging is essential for effective treatment and improved patient outcomes. Recent advancements in machine learning have enabled automated diagnostic tools that assist radiologists in making more reliable and efficient decisions. In this work, we propose a Singular Value Decomposition-based Least Squares (SVD-LS) framework for multi-class pneumonia classification, leveraging powerful feature representations from state-of-the-art self-supervised and transfer learning models. Rather than relying on computationally expensive gradient-based fine-tuning, we employ a closed-form, non-iterative classification approach that ensures efficiency without compromising accuracy. Experimental results demonstrate that SVD-LS achieves competitive performance while offering significantly reduced computational costs, making it a viable alternative for real-time medical imaging applications. The implementation is available at: github.com/meterdogan07/SVD-LS.
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