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.

本论文提出了一种用于医学图像层次多标签分类的转移学习与最大约束模块网络，通过解决数据不平衡和层次约束等问题，提高了多标签预测准确度，在医疗应用领域具有重要价值。