TLMCM Network for Medical Image Hierarchical Multi-Label Classification
- URL: http://arxiv.org/abs/2311.00282v2
- Date: Sat, 11 Nov 2023 08:16:29 GMT
- Title: TLMCM Network for Medical Image Hierarchical Multi-Label Classification
- Authors: Meng Wu, Siyan Luo, Qiyu Wu, Wenbin Ouyang
- Abstract summary: Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare.
This paper proposes Transfer Learning with Maximum Constraint Module (TLMCM) network for the MI-HMC task.
Experimental results demonstrate that the TLMCM network achieves high multi-label prediction accuracy($80%$-$90%$) for MI-HMC tasks.
- Score: 5.338183364083318
- License: http://creativecommons.org/licenses/by/4.0/
- 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($AU\overline{(PRC)}$) 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.
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