论文标题
发行：使用深度对抗网络检测带有不均匀照明的伸长接触细胞
DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network
论文作者
论文摘要
梭状芽胞杆菌艰难梭菌感染（C. diff）是美国医院患者的继发性感染最常见的死亡原因。在扫描电子显微镜（SEM）图像中检测C. diff细胞是量化不足处理处理功效的重要任务。但是，由于存在不均匀照明和遮挡，检测SEM图像中的C. diff细胞是一个具有挑战性的问题。照明归一化预处理步骤会破坏纹理并为图像增加噪声。此外，细胞通常聚集在一起，导致接触细胞和闭塞。在本文中，提出了一种使用对抗训练的深细胞检测方法，提出了一种对对抗性训练，特别是对不均匀的照明和遮挡。开发了一个对抗网络来提供区域建议并将建议传递给特征提取网络。此外，开发了一个修改的IOU度量，以允许在各种方向检测接触细胞。结果表明，在SEM图像中检测到接触细胞的最先进的表现至少提高了平均平均精度20％。
Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.
