Multiple myeloma (MM) is a type of blood cancer where plasma cells abnormally multiply and crowd out regular blood cells in the bones. Automated analysis of bone marrow smear examination is considered promising to improve the performance and reduce the labor cost in MM diagnosis. To address the drawbacks in established methods, which mainly aim at identifying monoclonal plasma cells (monoclonal PCs) via binary classification, in this work, considering that monoclonal PCs is not the only basis in MM diagnosis, for the first we construct a multi-object detection model for MM diagnosis. The experimental results show that our model can handle the images at a throughput of 80 slides/s and identify six lineages of bone marrow cells with an average accuracy of 90.8%. This work makes a step further toward full-automatic and high-efficiency MM diagnosis.

中文翻译：

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基于多目标检测的多发性骨髓瘤高通量高精度诊断

多发性骨髓瘤 (MM) 是一种血癌，其中浆细胞异常繁殖并排挤骨骼中的正常血细胞。骨髓涂片检查的自动化分析被认为有希望提高 MM 诊断的性能并降低人工成本。为了解决现有方法的缺点，主要旨在通过二进制分类识别单克隆浆细胞（单克隆 PC），在这项工作中，考虑到单克隆 PC 不是 MM 诊断的唯一基础，首先我们构建了一个多目标MM诊断的检测模型。实验结果表明，我们的模型可以以 80 张幻灯片/秒的吞吐量处理图像，并以 90.8% 的平均准确度识别 6 个骨髓细胞谱系。这项工作使全自动和高效的 MM 诊断更进一步。