Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development

END-TO-END PREDICTION OF WELD PENETRATION IN REAL TIME BASED ON DEEP LEARNING

Welding is an important joining technique that has been automated/robotized. In automated/robotic welding applications, however, the parameters are preset and are not adaptively adjusted to overcome unpredicted disturbances, which cause these applications to not be able to meet the standards from welding/manufacturing industry in terms of quality, efficiency, and individuality. Combining information sensing and processing with traditional welding techniques is a significant step toward revolutionizing the welding industry. In practical welding, the weld penetration as measured by the back-side bead width is a critical factor when determining the integrity of the weld produced. However, the back-side bead width is difficult to be directly monitored during manufacturing because it occurs underneath the surface of the welded workpiece. Therefore, predicting back-side bead width based on conveniently sensible information from the welding process is a fundamental issue in intelligent welding. Traditional research methods involve an indirect process that includes defining and extracting key characteristic information from the sensed data and building a model to predict the target information from the characteristic information. Due to a lack of feature information, the cumulative error of the extracted information and the complex sensing process directly affect prediction accuracy and real-time performance. An end-to-end, data-driven prediction system is proposed to predict the weld penetration status from top-side images during welding. In this method, a passive-vision sensing system with two cameras to simultaneously monitor the top-side and back-bead information is developed. Then the weld joints are classified into three classes (i.e., under penetration, desirable penetration, and excessive penetration) according to the back-bead width. Taking the weld pool-arc images as inputs and corresponding penetration statuses as labels, an end-to-end convolutional neural network (CNN) is designed and trained so the features are automatically defined and extracted. In order to increase accuracy and training speed, a transfer learning approach based on a residual neural network (ResNet) is developed. This ResNet-based model is pre-trained on an ImageNet dataset to process a better feature-extracting ability, and its fully connected layers are modified based on our own dataset. Our experiments show that this transfer learning approach can decrease training time and improve performance. Furthermore, this study proposes that the present weld pool-arc image is fused with two previous images that were acquired 1/6s and 2/6s earlier. The fused single image thus reflects the dynamic welding phenomena, and prediction accuracy is significantly improved with the image-sequence data by fusing temporal information to the input layer of the CNN (early fusion). Due to the critical role of weld penetration and the negligible impact on system implementation, this method represents major progress in the field of weld-penetration monitoring and is expected to provide more significant improvements during welding using pulsed current where the process becomes highly dynamic

Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells—neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons’ sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series “24”. First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare “computer vision” with “neuronal vision”—footprints associated with each character present in the activity of a subset of neurons—and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants’ subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL