论文标题
莫奈：生物医学图像中复制检测的多尺度重叠网络
MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images
论文作者
论文摘要
对生物医学图像进行操纵以歪曲实验结果，困扰着生物医学群落。最近对问题的兴趣导致了数据集和相关任务的策划，以促进生物医学法医方法的发展。其中，最大的操纵检测任务集中在图像之间重复区域的检测上。基于自然图像训练的传统计算机视觉模型并非旨在克服生物医学图像带来的挑战。我们提出了一个多尺度重叠检测模型，以检测重复的图像区域。我们的模型的结构是从层次上找到重复，以减少补丁操作的数量。它总体上和多个生物医学图像类别实现了最先进的性能。
Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dataset and associated tasks to promote the development of biomedical forensic methods. Of these, the largest manipulation detection task focuses on the detection of duplicated regions between images. Traditional computer-vision based forensic models trained on natural images are not designed to overcome the challenges presented by biomedical images. We propose a multi-scale overlap detection model to detect duplicated image regions. Our model is structured to find duplication hierarchically, so as to reduce the number of patch operations. It achieves state-of-the-art performance overall and on multiple biomedical image categories.
