Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning

• URL: http://arxiv.org/abs/2410.17494v1
• Date: Wed, 23 Oct 2024 01:25:25 GMT
• Title: Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning
• Authors: Jun-En Ding, Chien-Chin Hsu, Feng Liu,
• Abstract summary: This paper proposes a novel Cross-Graph Modal Contrastive Learning framework for multimodal medical image classification. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction.
• Score: 5.660131312162423
• License:
• Abstract: The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multimodal medical image classification. The model effectively integrates both image and non-image data by constructing cross-modality graphs and leveraging contrastive learning to align multimodal features in a shared latent space. An inter-modality feature scaling module further optimizes the representation learning process by reducing the gap between heterogeneous modalities. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction. Additionally, the method shows superior performance in multi-class melanoma classification. The CGMCL framework provides valuable insights into medical image classification while offering improved disease interpretability and predictive capabilities.

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