Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

Abstract

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

“…As allergic diseases are influenced by environmental factors such as PM 10 , capturing spatial dependence is the main problem in predicting the number of patients with allergic diseases. A convolutional neural network (CNN) [ 32 – 34 ] can capture local spatial features when analyzing medical images or records represented by regular Euclidean data. However, the population of patients with allergic diseases in each region exhibits irregularly structured data, similar to gene interaction networks and chemical molecular structures, which means that they are graph-structured data rather than regular grid data to which CNNs can be applied.…”