通过解耦表示实现眼科疾病分级的鲁棒多模态学习
Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation
AI总结：
针对多模态眼科数据不完整及特征冗余纠缠问题，提出EDRL策略，通过自蒸馏机制结合特征选择与解耦表示，显著提升了眼科疾病分级的鲁棒性与可解释性。
摘要：
本文讨论了眼科医生如何经常依赖多模态数据来提高诊断准确性。然而，由于医疗设备的缺乏和对数据隐私的担忧，现实应用中完整的多模态数据十分罕见。传统深度学习方法通常通过在潜在空间中学习表示来解决这些问题。然而，本文强调了这些方法的两个关键局限性：(i) 复杂模态中与任务无关的冗余信息（例如，大量切片）导致潜在空间表示中出现显著冗余。(ii) 重叠的多模态表示使得难以提取每种模态的独特特征。为了克服这些挑战，作者提出了Essence-Point and Disentangle Representation Learning (EDRL)策略，该策略将自蒸馏机制集成到端到端框架中，以增强特征选择和解耦，从而实现更鲁棒的多模态学习。具体而言，Essence-Point Representation Learning模块选择判别性特征，以提高疾病分级性能。Disentangled Representation Learning模块将多模态数据分离为模态共有和模态独特表示，减少了特征纠缠，并增强了眼科疾病诊断的鲁棒性和可解释性。在多模态眼科数据集上的实验表明，所提出的EDRL策略显著优于当前最先进的方法。
英文摘要：
This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. However, the paper highlights two key limitations of these approaches: (i) Task-irrelevant redundant information (e.g., numerous slices) in complex modalities leads to significant redundancy in latent space representations. (ii) Overlapping multimodal representations make it difficult to extract unique features for each modality. To overcome these challenges, the authors propose the Essence-Point and Disentangle Representation Learning (EDRL) strategy, which integrates a self-distillation mechanism into an end-to-end framework to enhance feature selection and disentanglement for more robust multimodal learning. Specifically, the Essence-Point Representation Learning module selects discriminative features that improve disease grading performance. The Disentangled Representation Learning module separates multimodal data into modality-common and modality-unique representations, reducing feature entanglement and enhancing both robustness and interpretability in ophthalmic disease diagnosis. Experiments on multimodal ophthalmology datasets show that the proposed EDRL strategy significantly outperforms current state-of-the-art methods.
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。
