Cioffi Gaia, Dannhauser David, Rossi Domenico, Netti Paolo A, Causa Filippo
Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.
Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy.
Biomed Opt Express. 2023 Sep 6;14(10):5060-5074. doi: 10.1364/BOE.492028. eCollection 2023 Oct 1.
Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.
基于神经网络的图像分类在生命科学应用中被广泛使用。然而，对于未知图像推断出一种正确的分类方法至关重要，因为在这种情况下无法利用先验知识。在封闭集假设下，未知图像将不可避免地被错误分类，但通过选择一种开放集分类方法可以真正克服这一问题，该方法首先生成已识别图像的分布内数据，以连续区分分布外图像。在生命科学场景中对单细胞应用的此类图像分类测试尚未进行，但这可能会拓宽我们在量化深度学习中预测不确定性影响方面的专业知识。在此框架下，我们在活细胞的散射快照上实现了开放集概念，以区分未知和已知的细胞类别，目标是四种不同的已知单核细胞类别和一种单一的肿瘤未知单核细胞系。我们还研究了对实验样本误差的影响，并优化了神经网络超参数，以获得较高的未知细胞类别检测准确率。我们发现我们的开放集方法对样本噪声具有鲁棒性，这是其在生命科学中应用的一个关键方面。此外，所提出的基于开放集的神经网络揭示了细胞预测之外的测量不确定性，这可应用于广泛的单细胞分类。
