基于渐进解耦感知对比学习的多模态医学内窥镜图像分析
Multimodal Medical Endoscopic Image Analysis via Progressive Disentangle-aware Contrastive Learning
AI总结：
本文提出一种基于“对齐-解耦-融合”机制的多模态表示学习框架，整合WLI与NBI内窥镜图像，通过多尺度分布对齐和渐进式特征解耦提升咽喉部肿瘤分割精度。
摘要：
咽喉部肿瘤的精确分割对于精准诊断和有效治疗规划至关重要。然而，传统的单模态成像方法往往难以捕捉这些肿瘤复杂的解剖和病理特征。在本研究中，我们提出了一种创新的多模态表示学习框架，该框架基于“对齐-解耦-融合”机制，无缝整合2D白光成像（WLI）和窄带成像（NBI）图像对，以提升分割性能。我们方法的一个核心是多尺度分布对齐，它通过在多个transformer层之间对齐特征来缓解模态差异。此外，我们开发了一种渐进式特征解耦策略，通过设计的初步解耦和解耦感知对比学习，有效分离模态特定特征和共享特征，从而实现鲁棒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的方法，在多样化的真实临床场景中取得了卓越的准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
