论文标题
基于显微镜的疟疾检测深度无监督学习
Deep unsupervised learning for Microscopy-Based Malaria detection
论文作者
论文摘要
疟疾是由寄生虫引起的一种蚊子传播疾病，每年在全球造成超过100万人。如果没有治疗，人们可能会造成严重的并发症，导致死亡。有效而准确的诊断对于疟疾的管理和控制很重要。我们的研究重点是利用机器学习来提高疟疾诊断的效率。我们利用修改的U-NET体系结构作为无监督的学习模型来进行细胞边界检测。然后，通过Mahalanobis距离算法在色彩空间中鉴定出感染疟疾的血细胞。细胞分割和疟疾检测过程均经常需要密集的手动标签，我们希望通过无监督的工作流量消除。
Malaria, a mosquito-borne disease caused by a parasite, kills over 1 million people globally each year. People, if left untreated, may develop severe complications, leading to death. Effective and accurate diagnosis is important for the management and control of malaria. Our research focuses on utilizing machine learning to improve the efficiency in Malaria diagnosis. We utilize a modified U-net architecture, as an unsupervised learning model, to conduct cell boundary detection. The blood cells infected by malaria are then identified in chromatic space by a Mahalanobis distance algorithm. Both the cell segmentation and Malaria detection process often requires intensive manual label, which we hope to eliminate via the unsupervised workflow.
