Safeguarding Medical Image Segmentation Datasets against Unauthorized
  Training via Contour- and Texture-Aware Perturbations
Xun Lin Yi Yu Song Xia Jue Jiang Haoran Wang Zitong Yu Yizhong Liu Ying Fu Shuai Wang Wenzhong Tang Alex Kot
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%).