基于DenseNet的少样本跨数据集结核检测

中文摘要 AI 辅助

结核病（TB）是最常见且危险的细菌性疾病之一，每年在全球造成大量死亡。尽管许多深度学习模型能从胸部X光片相当准确地检测结核病，但不同数据集间严重的域偏移使该任务极具挑战性，各域间不同的成像协议、患者人口统计数据和设备导致泛化困难。在实际场景中，模型可能在一个数据集上表现良好，但在另一个数据集上测试时性能会明显下降。本研究通过少样本规模研究解决该域适应挑战，采用TBX11K作为源域、Mendeley TB数据集作为目标域开展受控跨数据集评估，研究目标样本数量变化在三种训练机制下对模型性能的影响：冻结骨干网络的适应、源预训练DenseNet121模型的全微调、从头开始训练。结果表明，模型即使在数据有限时也能表现良好，每类仅用75个标注样本即可达到98.36%的准确率；适应曲线显示微调能有效缓解域偏移。这些发现确立了预训练模型的全微调作为低资源临床部署场景中缓解域偏移的高效实用策略。

英文摘要

Tuberculosis (TB) is one of the most common and dangerous bacterial ailments. Every year, it causes a large number of deaths worldwide. Although many deep learning models can detect tuberculosis from chest X-rays quite accurately, severe domain shift across datasets makes the task challenging. Different imaging protocols, patient demographics, and equipment across domains make the task of generalization difficult. In real-world settings, a model may perform well on one dataset but show a noticeable drop in performance when tested on another. In this work, we address this domain adaptation challenge through a few-shot scaling study. A controlled cross-dataset evaluation is presented in this paper using TBX11K as the source domain and the Mendeley TB dataset as the target domain. It is investigated how varying the number of target samples affects model performance under three training regimes: frozen backbone adaptation, full fine-tuning of a source-pretrained DenseNet121 model, and training from scratch. The results indicate that the model can perform well even with limited data and can achieve 98.36\% accuracy with just 75 labeled samples per class. The adaptation curves demonstrate how fine-tuning effectively mitigates domain shift. These findings establish full fine-tuning of pretrained models as a highly effective and practical strategy for mitigating domain shift in low-resource clinical deployment scenarios.

发表机构

• Dhaka International University（达卡国际大学）

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