TLMCM Network for Medical Image Hierarchical Multi-Label Classification

Abstract

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(PRC)AU\overline{(PRC)}) metric. In addition, this research proposes two novel accuracy metrics, EMREMR and HammingAccuracyHammingAccuracy, 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%80\%-90%90\%) for MI-HMC tasks, making it a valuable contribution to healthcare domain applications

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