论文标题
Miinet：用于支持医学诊断的图像质量改进框架
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
论文作者
论文摘要
医疗图像是必不可少的，并且可以支持医学专家做出诊断决策。但是，拍摄的医学图像，尤其是喉咙和内窥镜图像通常是朦胧，缺乏焦点或不均匀的照明。因此，这些可能困难的医生诊断过程。在本文中，我们提出了Miinet，这是一种新型的图像到图像翻译网络，用于通过将低质量图像翻译成高质量的清洁版本来改善医疗图像的质量。我们的Miinet不仅能够产生高分辨率的清洁图像，而且还可以保留原始图像的属性，从而使诊断对医生更有利。对脱掩的100次实用喉咙图像进行的实验表明，我们的MIINET很大程度上改善了平均医生意见评分（MDO），该评分评估了2.36至4.11的图像的质量和可重复性，而Cyclegan的Dehazed Images的得分较低3.83。三位医生证实了MIINET，可以满足原始低质量图像的喉咙诊断。
Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy images are normally hazy, lack of focus, or uneven illumination. Thus, these could difficult the diagnosis process for doctors. In this paper, we propose MIINet, a novel image-to-image translation network for improving quality of medical images by unsupervised translating low-quality images to the high-quality clean version. Our MIINet is not only capable of generating high-resolution clean images, but also preserving the attributes of original images, making the diagnostic more favorable for doctors. Experiments on dehazing 100 practical throat images show that our MIINet largely improves the mean doctor opinion score (MDOS), which assesses the quality and the reproducibility of the images from the baseline of 2.36 to 4.11, while dehazed images by CycleGAN got lower score of 3.83. The MIINet is confirmed by three physicians to be satisfying in supporting throat disease diagnostic from original low-quality images.
