面向不平衡数据集的红细胞分割：重叠细胞分离与分类
Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset
AI总结：
针对血涂片红细胞分类中的细胞重叠与数据不平衡问题，提出基于凹点检测和有向椭圆拟合的重叠细胞分割方法，结合权重平衡与数据增强的EfficientNet-B1实现了更优的分类效果。
摘要：
血涂片图像上的自动化红细胞（RBC）分类可帮助血液科医生以更短的时间、更低的成本分析红细胞实验室检测结果。但重叠细胞会导致错误的预测结果，因此在分类前必须将其分割为多个单个红细胞。使用深度学习进行多类别分类时，医学影像领域普遍存在数据不平衡问题，因为正常样本的数量始终多于罕见病样本。\n本文提出一种从血涂片图像中分割并分类红细胞的新方法，专门用于解决细胞重叠与数据不平衡问题。针对重叠细胞分离任务，我们的分割流程首先估计代表红细胞的椭圆：该方法先检测凹点，再使用有向椭圆拟合得到椭圆。在20张血涂片图像上的分割准确率为0.889。\n分类任务需要平衡的训练数据集，但部分红细胞类型十分罕见。本数据集包含20875个独立红细胞样本的12类红细胞，不平衡比为34.538，因此使用机器学习处理不平衡数据集下的红细胞分类，比许多其他应用场景更具挑战性。我们分析了应对该问题的多种技术，采用带数据增强的EfficientNet-B1取得了最优效果，准确率和F1分数分别为0.921和0.8679。实验结果表明，结合数据增强的权重平衡技术可提升少数类的F1分数，具备解决不平衡问题的潜力，而数据增强则显著提升了整体分类性能。
英文摘要：
Automated red blood cell (RBC) classification on blood smear images helps hematologists to analyze RBC lab results in a reduced time and cost. However, overlapping cells can cause incorrect predicted results, and so they have to be separated into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify RBCs from blood smear images, specifically to tackle cell overlapping and data imbalance problems. Focusing on overlapping cell separation, our segmentation process first estimates ellipses to represent RBCs. The method detects the concave points and then finds the ellipses using directed ellipse fitting. The accuracy from 20 blood smear images was 0.889. Classification requires balanced training datasets. However, some RBC types are rare. The imbalance ratio of this dataset was 34.538 for 12 RBC classes from 20,875 individual RBC samples. The use of machine learning for RBC classification with an imbalanced dataset is hence more challenging than many other applications. We analyzed techniques to deal with this problem. The best accuracy and F1-score were 0.921 and 0.8679, respectively, using EfficientNet-B1 with augmentation. Experimental results showed that the weight balancing technique with augmentation had the potential to deal with imbalance problems by improving the F1-score on minority classes, while data augmentation significantly improved the overall classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
