论文标题
使用动态SSD-GAN检测小息肉
Detecting small polyps using a Dynamic SSD-GAN
论文作者
论文摘要
内窥镜检查用于检查可能发展为癌症的息肉的喉咙，胃和肠。可以训练机器学习系统来处理结肠镜检查并检测息肉。但是，这些系统在图像中看起来很小的物体上的性能往往很差。这里显示的是，将单杆检测器与对抗训练的发电机相结合，以提高样本小区域建议，可以显着改善视觉上微型息肉的检测。与传统的FCN基线相比，本文引入的动态SSD-GAN管道对视觉微息肉的敏感性提高了12％。
Endoscopic examinations are used to inspect the throat, stomach and bowel for polyps which could develop into cancer. Machine learning systems can be trained to process colonoscopy images and detect polyps. However, these systems tend to perform poorly on objects which appear visually small in the images. It is shown here that combining the single-shot detector as a region proposal network with an adversarially-trained generator to upsample small region proposals can significantly improve the detection of visually-small polyps. The Dynamic SSD-GAN pipeline introduced in this paper achieved a 12% increase in sensitivity on visually-small polyps compared to a conventional FCN baseline.
