面向人工标记去除的具备目标感知判别能力的盲修复
Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal
AI总结：
针对医学图像人工标记干扰AI诊断且现有修复需手动掩码的问题，提出含双分支重建网络与目标感知判别器的盲修复方法，在三类医学数据集上表现优于SOTA方法。
摘要：
医学图像常带有医生添加的标记，这些标记会干扰基于AI的诊断。这一问题凸显了利用修复技术还原被破坏视觉内容的必要性。但现有方法需要手动标注掩码作为输入，限制了应用场景。本文提出一种新型盲修复方法，无需掩码输入作为引导，即可自动重建受损区域内的视觉内容。我们的模型包含一个盲重建网络和一个用于对抗训练的目标感知判别器。重建网络设有两个分支，分别预测图像中的受损区域并同时还原缺失的视觉内容。借助密集目标检测器的强大识别能力，目标感知判别器可确保修复后标记无法被检测到，从而使还原后的图像与干净图像高度相似。我们在三种不同医学成像模态的数据集上评估了该方法，证实其性能优于其他现有最优方法。
英文摘要：
Medical images often incorporate doctor-added markers that can hinder AI-based diagnosis. This issue highlights the need of inpainting techniques to restore the corrupted visual contents. However, existing methods require manual mask annotation as input, limiting the application scenarios. In this paper, we propose a novel blind inpainting method that automatically reconstructs visual contents within the corrupted regions without mask input as guidance. Our model includes a blind reconstruction network and an object-aware discriminator for adversarial training. The reconstruction network contains two branches that predict corrupted regions in images and simultaneously restore the missing visual contents. Leveraging the potent recognition capability of a dense object detector, the object-aware discriminator ensures markers undetectable after inpainting. Thus, the restored images closely resemble the clean ones. We evaluate our method on three datasets of various medical imaging modalities, confirming better performance over other 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。
