Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and \textit{hierarchy constraint}. Existing solutions involve complex model architecture design or domain-specific preprocessing, demanding considerable expertise or effort in implementation. To address these limitations, this paper proposes Transfer Learning with Maximum Constraint Module (TLMCM) network for the MI-HMC task. The TLMCM network offers a novel approach to overcome the aforementioned challenges, outperforming existing methods based on the Area Under the Average Precision and Recall Curve($AU\overline{(PRC)}$) metric. In addition, this research proposes two novel accuracy metrics, $EMR$ and $HammingAccuracy$, which have not been extensively explored in the context of the MI-HMC task. Experimental results demonstrate that the TLMCM network achieves high multi-label prediction accuracy($80\%$-$90\%$) for MI-HMC tasks, making it a valuable contribution to healthcare domain applications.

翻译：医学图像层次化多标签分类（MI-HMC）在现代医疗中至关重要，面临数据不平衡与层次约束两大挑战。现有解决方案需要复杂的模型架构设计或领域特定的预处理，对实现者的专业知识或投入要求较高。为克服这些局限，本文针对MI-HMC任务提出了带有最大约束模块的迁移学习（TLMCM）网络。TLMCM网络提供了一种创新方法以应对上述挑战，在平均精确率召回率曲线下面积（$AU\overline{(PRC)}$）指标上优于现有方法。此外，本研究提出了两种新型准确率度量指标$EMR$和$HammingAccuracy$，这些指标在MI-HMC任务背景下尚未得到广泛探索。实验结果表明，TLMCM网络在MI-HMC任务中取得了较高的多标签预测准确率（$80\%$-$90\%$），为医疗领域应用做出了重要贡献。