论文标题
用于patasitic卵检测和分类的强大合奏模型
A Robust Ensemble Model for Patasitic Egg Detection and Classification
论文作者
论文摘要
肠道寄生虫感染是全球发病率的主要原因，仍然缺乏节省时间，高敏性和用户友好的检查方法。深度学习技术的发展揭示了其在生物形象中的广泛应用潜力。在本文中，我们应用了几个对象检测器，例如Yolov5和变体CascaderCnns，以自动区分显微镜图像中的寄生卵。通过特殊设计的优化，包括原始数据扩展，模型集合，传输学习和测试时间扩展，我们的模型在挑战数据集上实现了出色的性能。此外，我们的模型对噪声进行了训练，对污染的输入增强了较高的鲁棒性，从而进一步扩大了其实践中的适用性。
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.
