论文标题
通过在成像方式之间转换来改善内窥镜决策支持系统
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
论文作者
论文摘要
新颖的成像技术提出了许多有关适应计算机辅助决策支持系统的问题。分类模型需要进行调整，甚至需要从头开始训练，以利用增强技术的全部潜力。这两个选项通常都需要获取新标记的培训数据。在这项工作中，我们研究了图像到图像翻译对内窥镜图像的适用性，显示了不同的成像方式，即常规的白光和窄带成像。在一项有关计算机辅助乳糜泻诊断的研究中，我们探讨了图像到图像翻译是否能够有效地进行域之间的翻译。我们研究是否可以使用有限的标记培训数据的设置进行虚拟（或虚拟和真实）样品的虚拟（或真实）样品的混合物进行培训。最后，我们还询问测试图像向另一个域的翻译是否能够通过利用增强的成像特性来提高准确性。
Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly trained from scratch to exploit the full potential of enhanced techniques. Both options typically require the acquisition of new labeled training data. In this work we investigate the applicability of image-to-image translation to endoscopic images showing different imaging modalities, namely conventional white-light and narrow-band imaging. In a study on computer-aided celiac disease diagnosis, we explore whether image-to-image translation is capable of effectively performing the translation between the domains. We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data. Finally, we also ask whether a translation of testing images to another domain is capable of improving accuracy by exploiting the enhanced imaging characteristics.
