Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method. The development of deep learning technique reveals its broad application potential in biological image. In this paper, we apply several object detectors such as YOLOv5 and variant cascadeRCNNs to automatically discriminate parasitic eggs in microscope images. Through specially-designed optimization including raw data augmentation, model ensemble, transfer learning and test time augmentation, our model achieves excellent performance on challenge dataset. In addition, our model trained with added noise gains a high robustness against polluted input, which further broaden its applicability in practice.
翻译:作为全世界发病的一个主要原因,恶性寄生虫感染仍然缺乏节省时间、高度敏锐和方便用户的检查方法。深层学习技术的发展揭示了其在生物图像中的广泛应用潜力。在本文件中,我们运用了YOLOv5和变异型级级级级级级级寄生虫子探测器等几个物体探测器,在显微镜图像中自动歧视寄生虫蛋。通过特别设计的优化,包括原始数据增强、模型组合、传输学习和测试时间增强,我们的模型在挑战数据集上取得了卓越的成绩。此外,我们经过添加噪音培训的模型在防止被污染投入方面获得了高度的活力,从而进一步扩大了其实际应用性。