The diagnosis of brain tumors is pivotal for effective treatment, with MRI serving as a commonly used non-invasive diagnostic modality in clinical practices. Fundamentally, brain tumor diagnosis is a type of pattern recognition task that requires the integration of information from multi-modal MRI images. However, existing fusion strategies are hinder by the scarcity of multi-modal imaging samples. In this paper, we propose a new training paradigm tailored for the scenario of multi-modal imaging in brain tumor diagnosis, called multi-modal supervised contrastive learning method (MMSupcon). This method significantly enhances diagnostic accuracy through two key components: multi-modal medical image fusion and multi-modal supervised contrastive loss. First, the fusion component integrates complementary imaging modalities to generate information-rich samples. Second, by introducing fused samples to guide …
本文针对脑肿瘤多模态MRI诊断中融合策略受限于样本稀缺的问题，提出多模态监督对比学习方法MMSupcon。该方法通过多模态医学图像融合生成信息丰富的样本，并设计多模态监督对比损失，引导模型有效整合互补模态信息，提升诊断准确性。