Multi label method improves blood cell and aggregate identification
Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels
Computer Vision and Pattern RecognitionMachine Learning
Summary
Blood cell analysis often relies on identifying each cell as only one type, but this can miss groups of cells stuck together called aggregates. The authors compare two ways to classify these images: one that assigns just one label per image and one that allows multiple labels at once. They found that the multi-label method better identifies aggregates even if those specific groups weren't shown during training. This could help improve medical tests that need to analyze complex blood samples.
flow cytometrydeformability cytometrymulti-class classificationmulti-label classificationcell aggregatescell deformabilityimage classificationblood cell typesmachine learningmicrofluidics
Authors
Igor Zingman, Shada Abuhattum, Sara Kaliman, Maximilian Schlögel, Paul Müller, Markéta Kubánková, Nadine Ströhlein, Manuela Hauke, Lena Schnörer, Martin Kräter, Jochen Guck
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.