The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding unauthorized training of AI systems for commercial purposes and the duties of patient privacy protection have led numerous institutions to hesitate to share their images. This is particularly true for medical image segmentation (MIS) datasets, where the processes of collection and fine-grained annotation are time-intensive and laborious. Recently, Unlearnable Examples (UEs) methods have shown the potential to protect images by adding invisible shortcuts. These shortcuts can prevent unauthorized deep neural networks from generalizing. However, existing UEs are designed for natural image classification and fail to protect MIS datasets imperceptibly as their protective perturbations are less learnable than important prior knowledge in MIS, e.g., contour and texture features. To this end, we propose an Unlearnable Medical image generation method, termed UMed. UMed integrates the prior knowledge of MIS by injecting contour- and texture-aware perturbations to protect images. Given that our target is to only poison features critical to MIS, UMed requires only minimal perturbations within the ROI and its contour to achieve greater imperceptibility (average PSNR is 50.03) and protective performance (clean average DSC degrades from 82.18% to 6.80%).

翻译：公开可获取的医学图像广泛普及，显著推动了多个研究与临床领域的发展。然而，针对AI系统为商业目的进行未授权训练以及患者隐私保护责任的担忧，导致众多机构在共享图像时犹豫不决。这一问题在医学图像分割（MIS）数据集中尤为突出，因为其采集与细粒度标注过程耗时费力。近期，"不可学习样本"（UEs）方法通过添加不可见捷径展现出保护图像的潜力，此类捷径可阻止未授权的深度神经网络进行泛化。然而，现有UEs方法专为自然图像分类设计，在保护MIS数据集时难以实现不可感知性——其保护性扰动相对于MIS中重要的先验知识（如轮廓与纹理特征）可学习性较弱。为此，我们提出一种名为UMed的不可学习医学图像生成方法。UMed通过注入轮廓与纹理感知扰动，整合MIS的先验知识以保护图像。鉴于我们的目标是仅破坏MIS关键特征，UMed仅需在ROI及其轮廓区域施加最小扰动，即可实现更优的不可感知性（平均PSNR达50.03）和保护性能（干净数据平均DSC从82.18%降至6.80%）。