# TLMCM Network for Medical Image Hierarchical Multi-Label Classification

arXiv: https://arxiv.org/abs/2311.00282 · HTML: https://mlsanity.com/paper/2311.00282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting](https://mlsanity.com/paper/2107.02104/) (2021)
- [HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach](https://mlsanity.com/paper/2006.07187/) (2020)
- [A Capsule Network for Hierarchical Multi-Label Image Classification](https://mlsanity.com/paper/2209.05723/) (2022)

## Abstract (arXiv)

Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and textithierarchy 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(AUoverline(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.