arXiv 2103.096712021-03-17cs.CVeess.IV

Percoll傅立叶变换增强遗传性溶血性贫血的CNN分类

Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Helmholtz Center Munich, German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Johns Hopkins University（约翰斯·霍普金斯大学）

Ario Sadafi, Lucía María Moya Sans, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr

备注：: Accepted for publication at the 2021 IEEE International Symposium on Biomedical Imaging (ISBI 2021)

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摘要：

遗传性溶血性贫血是影响红细胞形状和密度的遗传性疾病。目前用于诊断这种贫血的基因检测是昂贵的，并且在大多数临床实验室中不可用。在这里，我们提出了一种方法，用于识别遗传性溶血性贫血的基础上，一个标准的生物化学方法，称为Percoll梯度，获得了一个病人的血液。我们的混合方法包括使用空间数据驱动的功能，提取与卷积神经网络和光谱手工制作的功能从快速傅立叶变换。我们比较了AlexNet和VGG16架构的后期和早期特征融合。与其他方法相比，后期融合光谱特征的AlexNet表现更好。我们实现了平均F1评分为88%的不同类别的遗传性溶血性贫血的诊断Percoll梯度的可能性。最后，我们利用梯度CAM探索用于分类的空间特征。

英文摘要：

Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells. Genetic tests currently used to diagnose such anemias are expensive and unavailable in the majority of clinical labs. Here, we propose a method for identifying hereditary hemolytic anemias based on a standard biochemistry method, called Percoll gradient, obtained by centrifuging a patient's blood. Our hybrid approach consists on using spatial data-driven features, extracted with a convolutional neural network and spectral handcrafted features obtained from fast Fourier transform. We compare late and early feature fusion with AlexNet and VGG16 architectures. AlexNet with late fusion of spectral features performs better compared to other approaches. We achieved an average F1-score of 88% on different classes suggesting the possibility of diagnosing of hereditary hemolytic anemias from Percoll gradients. Finally, we utilize Grad-CAM to explore the spatial features used for classification.

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