Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).

Very large format video or wide-area motion imagery (WAMI) acquired by an airborne camera sensor array is characterized by persistent observation over a large field-of-view with high spatial resolution but low frame rates (i.e. one to ten frames per second). Current WAMI sensors have sufficient coverage and resolution to track vehicles for many hours using just a single airborne platform. We have developed an interactive low frame rate tracking system based on a derived rich set of features for vehicle detection using appearance modeling combined with saliency estimation and motion prediction. Instead of applying subspace methods to very high-dimensional feature vectors, we tested the performance of feature fusion to locate the target of interest within the prediction window. Preliminary results show that fusing the feature likelihood maps improves detection but fusing feature maps combined with saliency information actually degrades performance.

Convolutional neural networks (CNNs) have become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of the learned behavior from the underlying task of interest. This lack of transparency is a serious drawback, particularly in applications involving medical screening and diagnosis since poorly understood model behavior could adversely impact subsequent clinical decision-making. Recently, researchers have begun working on this issue and several methods have been proposed to visualize and understand the behavior of these models. We highlight the advantages offered through visualizing and understanding the weights, saliencies, class activation maps, and region of interest localizations in customized CNNs applied to the challenge of classifying parasitized and uninfected cells to aid in malaria screening. We provide an explanation for the models’ classification decisions. We characterize, evaluate, and statistically validate the performance of different customized CNNs keeping every training subject’s data separate from the validation set.

Key points: This study demonstrates rapid and dynamic changes in adhesion and cell elasticity following agonist stimulation that culminate in a remodelled cytoskeleton in vascular smooth muscle. Evidence is presented that the changes in adhesion and elasticity are coordinated and that these variables demonstrate temporal oscillation consisting of three major oscillation components. Eigen‐decomposition spectrum analysis revealed that these components of oscillation in cell elasticity and adhesion may be linked by shared signalling pathways. Evidence is provided that the agonists angiotensin II and adenosine produce remodelling of actin cytoskeleton that may alter the properties of the observed oscillations in elasticity and adhesion. It is concluded that angiotensin II and adenosine may regulate extracellular matrix adhesion and elasticity in vascular smooth muscle cells as a form of adaptation to more efficiently support contractile behaviour. In this study, we examined the ability of vasoactive agonists to induce dynamic changes in vascular smooth muscle cell (VSMC) elasticity and adhesion, and tested the hypothesis that these events are coordinated with rapid remodelling of the cortical cytoskeleton. Real‐time measurement of cell elasticity was performed with atomic force microscopy (AFM) and adhesion was assessed with AFM probes coated with fibronectin (FN). Temporal data were analysed using an Eigen‐decomposition method. Elasticity in VSMCs displayed temporal oscillations with three components at approximately 0.001, 0.004 and 0.07 Hz, respectively. Similarly, adhesion displayed a similar oscillatory pattern. Angiotensin II (ANG II, 10⁻⁶ m) increased (+100%) the amplitude of the oscillations, whereas the vasodilator adenosine (ADO, 10⁻⁴ m) reduced oscillation amplitude (–30%). To test whether the oscillatory changes were related to the architectural alterations in cortical cytoskeleton, the topography of the submembranous actin cytoskeleton (100–300 nm depth) was acquired with AFM. These data were analysed to compare cortical actin fibre distribution and orientation before and after treatment with vasoactive agonists. The results showed that ANG II increased the density of stress fibres by 23%, while ADO decreased the density of the stress fibres by 45%. AFM data were supported by Western blot and confocal microscopy. Collectively, these observations indicate that VSMC cytoskeletal structure and adhesion to the extracellular matrix are dynamically altered in response to agonist stimulation. Thus, vasoactive agonists probably invoke unique mechanisms that dynamically alter the behaviour and structure of both the VSMC cytoskeleton and focal adhesions to efficiently support the normal contractile behaviour of VSMCs.