Deep Learning and Handcrafted Features for Virus Image Classification

Abstract

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

“…This methodology was used in this work due to its excellent performance in solving several other classification problems, as introduced by [ 44 , 45 , 46 , 47 ].…”

Deep-learning in-situ classification of HIV-1 virion morphology

“…While the first aims to classify the pixels in an image predicting the probability that they belong to a certain class, object detection uses a perregion approach for classifying object instances. These techniques have been applied for the detection of cancer cell nuclei [18], the segmentation of neural membranes [19], segmentation of feline calcivirus [20] and virus classifications [21]. So-called Convolutional Neural Networks (CNNs) have proven useful for the semantic segmentation of small extracellular vesicles (sEVs) from TEM micrographs [22,23].…”

Machine Learning and Artificial Intelligence for the Prediction of Host–Pathogen Interactions: A Viral Case

“…[42][43][44] Various ML techniques may be employed to analyze HPIs subcellular data, ranging from support vector machines 45 to DL. [46][47][48][49][50] Infection manifestation on a single-cell level is typically hallmarked by the onset of cytopathic effect (CPE). 51,52 Synchronized with virus entry, uncoating, and replication through a virus genetic program, virus-induced CPE involves dramatic changes of cell morphology, 51,52 which can be observed in cell culture using conventional light microscopy.…”

“…Its manifestation often differs substantially for various pathogens, cell types, and multiplicity of infection (MOI). 57 Downstream tasks (Figure 2B) for which ML is employed on such data typically include pathogen image segmentation 49 (often using a variety of the U-Net architecture 58 ), HPI events or virus classification from full or cropped field-of-view, 47,48,50 pathogen object detection 59 or infection detection. 60 Other examples include understanding structure and function relationships with the pathogens 40 or time-lapse analysis.…”