TLMCM Network for Medical Image Hierarchical Multi-Label Classification
This work addresses medical image classification challenges for healthcare applications, but it appears incremental as it builds on transfer learning with a new constraint module.
The paper tackled the problem of Medical Image Hierarchical Multi-Label Classification (MI-HMC) by addressing data imbalance and hierarchy constraints, proposing the TLMCM network which achieved high multi-label prediction accuracy of 80%-90% and outperformed existing methods based on the AU(PRC) metric.
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.