Wang Haoran, Guo Xinyu, Song Kaiwen, Sun Mingyang, Shao Yanbin, Xue Songfeng, Zhang Hongwei, Zhang Tianyu
Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130012, China.
Neural Netw. 2025 Jan;181:106817. doi: 10.1016/j.neunet.2024.106817. Epub 2024 Oct 18.
Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oestrogen in menopausal women. Gradually, its symptoms worsen with age and prolonged menopausal status, which gravely impacts the quality of life as well as the physical and mental health of the patients. In this regard, optical coherence tomography (OCT) system effectively reduces the patient's burden in clinical diagnosis with its noncontact, noninvasive tomographic imaging process. Consequently, supervised computer vision models applied on OCT images have yielded excellent results for disease diagnosis. However, manual labeling on an extensive number of medical images is expensive and time-consuming. To this end, this paper proposes GO-MAE, a pretraining framework for self-supervised learning of GSM OCT images based on Masked Autoencoder (MAE). To the best of our knowledge, this is the first study that applies self-supervised learning methods on the field of GSM disease screening. Focusing on the semantic complexity and feature sparsity of GSM OCT images, the objective of this study is two-pronged: first, a dynamic masking strategy is introduced for OCT characteristics in downstream tasks. This method can reduce the interference of invalid features on the model and shorten the training time. In the encoder design of MAE, we propose a convolutional neural network and transformer parallel network architecture (C&T), which aims to fuse the local and global representations of the relevant lesions in an interactive manner such that the model can still learn the richer differences between the feature information without labels. Thereafter, a series of experimental results on the acquired GSM-OCT dataset revealed that GO-MAE yields significant improvements over existing state-of-the-art techniques. Furthermore, the superiority of the model in terms of robustness and interpretability was verified through a series of comparative experiments and visualization operations, which consequently demonstrated its great potential for screening GSM symptoms.
更年期泌尿生殖综合征（GSM）是一种由更年期女性雌激素水平降低引起的生理紊乱。随着年龄增长和更年期状态的延长，其症状会逐渐加重，严重影响患者的生活质量以及身心健康。在这方面，光学相干断层扫描（OCT）系统凭借其非接触、非侵入性的断层成像过程，有效减轻了患者在临床诊断中的负担。因此，应用于OCT图像的监督式计算机视觉模型在疾病诊断方面取得了优异成果。然而，对大量医学图像进行人工标注既昂贵又耗时。为此，本文提出了GO-MAE，这是一种基于掩码自动编码器（MAE）对GSM OCT图像进行自监督学习的预训练框架。据我们所知，这是第一项在GSM疾病筛查领域应用自监督学习方法的研究。针对GSM OCT图像的语义复杂性和特征稀疏性，本研究的目标有两个方面：第一，为下游任务中的OCT特征引入动态掩码策略。该方法可以减少无效特征对模型的干扰，缩短训练时间。在MAE的编码器设计中，我们提出了一种卷积神经网络和Transformer并行网络架构（C&T），旨在以交互方式融合相关病变的局部和全局表示，使模型在没有标签的情况下仍能学习到更丰富的特征信息差异。此后，在获取的GSM-OCT数据集上进行的一系列实验结果表明，GO-MAE比现有的最先进技术有显著改进。此外，通过一系列对比实验和可视化操作验证了该模型在鲁棒性和可解释性方面的优越性，从而证明了其在筛查GSM症状方面的巨大潜力。
