标题： 多模态医学内窥镜图像分析通过渐进式解缠感知对比学习 显示英文标题

标题： Multimodal Medical Endoscopic Image Analysis via Progressive Disentangle-aware Contrastive Learning

摘要： 准确分割喉咽部肿瘤对于精确诊断和有效治疗计划至关重要。 然而，传统的单模态成像方法往往难以捕捉这些肿瘤的复杂解剖和病理特征。 在本研究中，我们提出了一种基于“对齐-解耦-融合”机制的创新多模态表示学习框架，该框架无缝整合2D白光成像（WLI）和窄带成像（NBI）图像对，以提高分割性能。 我们方法的核心是多尺度分布对齐，通过在多个Transformer层之间对齐特征来减轻模态差异。 此外，设计了渐进式特征解耦策略，结合初步解耦和解耦感知对比学习，以有效分离模态特有和共享特征，从而实现稳健的多模态对比学习和高效的语义融合。 在多个数据集上的综合实验表明，我们的方法始终优于最先进方法，在多种真实临床场景中实现了更高的准确性。 显示英文摘要

摘要： Accurate segmentation of laryngo-pharyngeal tumors is crucial for precise diagnosis and effective treatment planning. However, traditional single-modality imaging methods often fall short of capturing the complex anatomical and pathological features of these tumors. In this study, we present an innovative multi-modality representation learning framework based on the `Align-Disentangle-Fusion' mechanism that seamlessly integrates 2D White Light Imaging (WLI) and Narrow Band Imaging (NBI) pairs to enhance segmentation performance. A cornerstone of our approach is multi-scale distribution alignment, which mitigates modality discrepancies by aligning features across multiple transformer layers. Furthermore, a progressive feature disentanglement strategy is developed with the designed preliminary disentanglement and disentangle-aware contrastive learning to effectively separate modality-specific and shared features, enabling robust multimodal contrastive learning and efficient semantic fusion. Comprehensive experiments on multiple datasets demonstrate that our method consistently outperforms state-of-the-art approaches, achieving superior accuracy across diverse real clinical scenarios.