微流控通道中血细胞及其聚集体的多标签与多类分类
Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels
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- Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）
- Max-Planck-Zentrum für Physik und Medizin（马克斯·普朗克物理与医学中心）
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中文总结 AI 辅助
本文利用变形性细胞术数据，提出多标签分类方法识别血细胞及其聚集体，克服多类分类无法识别未知聚集体的局限，简化注释并弥补临床分析缺口。
中文摘要 AI 辅助
变形性细胞术（DC）是一种成像流式细胞术，它使用配备摄像头的设备在高通量下测量细胞刚度以及其他细胞特性。面积和伸长率等细胞特性可以识别细胞类型，但这需要预先了解区分性特性，并且无法应用于临床上重要的细胞聚集体。利用DC数据，我们评估了传统的多类（MC）分类，并引入了一种多标签（ML）方法来识别血细胞及其聚集体。特别是，ML分类器可以同时为单个成像事件分配多个细胞类型标签。我们表明，与MC分类不同，ML分类可以识别训练数据中未代表的细胞聚集体。它还避免了对详尽、严格定义的聚集体标签的需求，从而简化和加速了注释。由于自动血液分析仪不能可靠地分析细胞聚集体，我们的方法可能有助于解决这一临床差距。
英文摘要
Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to other cellular properties at high throughput. Cellular properties such as area and elongation can identify cell types, but this requires prior knowledge of distinguishing properties and cannot be applied to clinically important cell aggregates. Using DC data, we evaluated conventional multi-class (MC) classification and introduced a multi-label (ML) approach for identifying blood cells and their aggregates. In particular, an ML classifier can simultaneously assign multiple cell-type labels to a single imaged event. We show that, unlike MC classification, ML classification can identify cell aggregates not represented in the training data. It also avoids the need for exhaustive, strictly defined aggregate labels, thereby simplifying and speeding up annotation. Since automated blood analyzers do not reliably analyze cell aggregates, our approach may help address this clinical gap.
