Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
Cell Rep Methods. 2021 Oct 25;1(6):100094. doi: 10.1016/j.crmeth.2021.100094.
The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.
机器学习方法在成像流式细胞术 (IFC) 数据中的应用具有改变血液疾病诊断的潜力。然而，机器学习模型训练需要手动标记的单细胞图像，这严重限制了其临床应用。为了解决这个问题，我们提出了 iCellCnn，这是一种基于无标记 IFC 的血液诊断的弱监督深度学习方法。我们证明了 iCellCnn 能够基于从人外周血单核细胞标本的荧光激活细胞分选后获得的 T 细胞的明场 IFC 图像，对 Sézary 综合征 (SS) 患者样本进行诊断。在四个健康供体和五个 SS 患者的样本大小下，iCellCnn 实现了 100%的分类准确率。由于 iCellCnn 不受 SS 诊断的限制，我们期望这种弱监督方法能够通过自动数据驱动的诊断来挖掘 IFC 的诊断潜力，从而对目前尚不清楚形态表现的疾病进行诊断。
