Dual-convolutional neural network-enhanced strain estimation method for optical coherence elastography

Convolutional Neural Networks Enable Direct Strain Estimation in Quasistatic Optical Coherence Elastography

Assessing the biomechanical properties of tissues can provide important information for disease diagnosis and therapeutic monitoring. Optical coherence elastography (OCE) is an emerging technology for measuring the biomechanical properties of tissues. Clinical translation of this technology is underway, and steps are being implemented to streamline data collection and processing. OCE data can be noisy, data processing can require significant manual tuning, and a single acquisition may contain gigabytes of data. In this work, we introduce a convolutional neural network-based method to translate raw OCE phase data to strain for quasistatic OCE that is ~40X faster than the conventional least squares approach by bypassing many intermediate data processing steps. The results suggest that a machine learning approach may be a valuable tool for fast, efficient, and accurate extraction of biomechanical information from raw OCE data.

Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e. self-management) have a significant impact on the development of their condition. Thus, the inclusion and discrimination of subsequent interventions according to their self-management is an urgent issue. A Sparse-representation-based Discriminative Classification model (SDC) is proposed in this paper to correctly classify MCI-DM patients based on their self-management ability. Specifically, an L1-minimization sparse representation model, an efficient machine learning model, is used to obtain the sparse histogram that encodes the identity of the test sample. Then, the coefficient of determination [Formula: see text] is adopted to determine the category based on the sparse histogram of the test sample. Extensive experiments on the self-management data of DM-MCI are conducted to verify the effectiveness of SDC. The experimental results show that the accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text], and F1-score [Formula: see text] are 94.3%, 95.0%, 94.3%, and 94.5%, respectively, demonstrating the excellent performance of SDC. The model used in this study has high accuracy and can be used for subgroup discrimination. The use of the sparse representation model in this study has supportive implications for the inclusion of research subjects in clinical intervention strategies.

Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning

Scanning electron microscopy (SEM) is a crucial tool for analyzing submicron-scale structures. However, the attainment of high-quality SEM images is contingent upon the high conductivity of the material due to constraints imposed by its imaging principles. For weakly conductive materials or structures induced by intrinsic properties or organic doping, the SEM imaging quality is significantly compromised, thereby impeding the accuracy of subsequent structure-related analyses. Moreover, the unavailability of paired high-low quality images in this context renders the supervised-based image processing methods ineffective in addressing this challenge. Here, an unsupervised method based on Cycle-consistent Generative Adversarial Network (CycleGAN) was proposed to enhance the quality of SEM images for weakly conductive samples. The unsupervised model can perform end-to-end learning using unpaired blurred and clear SEM images from weakly and well-conductive samples, respectively. To address the requirements of material structure analysis, an edge loss function was further introduced to recover finer details in the network-generated images. Various quantitative evaluations substantiate the efficacy of the proposed method in SEM image quality improvement with better performance than the traditional methods. Our framework broadens the application of artificial intelligence in materials analysis, holding significant implications in fields such as materials science and image restoration.