FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.

Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this process, however, high-performance algorithms have not been developed. The limitations in image acquisition, especially for intracellular parasites, make this process complex. For this reason, traditional image-processing methods are not easily transferred between different datasets and segmentation-based strategies do not have a high performance. Here, we propose a novel method FiCRoN, based on fully convolutional regression networks (FCRNs), as a promising new tool for estimating intracellular parasite burden. This estimation requires three values, intracellular parasites, infected cells and uninfected cells. FiCRoN solves this problem as multi-task learning: counting by regression at two scales, a smaller one for intracellular parasites and a larger one for host cells. It does not use segmentation or detection, resulting in a higher generalization of counting tasks and, therefore, a decrease in error propagation. Linear regression reveals an excellent correlation coefficient between manual and automatic methods. FiCRoN is an innovative freedom-respecting image analysis software based on deep learning, designed to provide a fast and accurate quantification of parasite burden, also potentially useful as a single-cell counter.

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation.

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human-robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.