Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels

2026-09-07

AI Summary

研究使用多标签分类方法解决血液细胞及其聚集体在微流控通道中的识别问题，相比传统的多类分类，该方法能更好地识别未出现在训练数据中的细胞聚集体。

Abstract

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.

Problem

• multi-label classification
• cell aggregates
• deformability cytometry

Innovation

• multi-label classification
• cell aggregates
• deformability cytometry