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Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems
CMA-Based design of a Novel structure for isolation enhancement and Radiation Pattern correction in MIMO antennas
Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc
Spatial heterogeneity of keyhole imagery coverage in China and imagery dataset cost estimation
Use of the Da Vinci SP surgical system in robot-assisted nipple-sparing mastectomy: a single-center, retrospective study
Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation
A multicenter study on TROP2 as a potential targeted therapy for extramammary Paget disease in Japan
Efficacy and safety of propranolol tablets vs. oral solution in neonates with severe infantile hemangiomas: a retrospective study in China
Association of triglyceride-glucose index with Helicobacter pylori infection in the 1999–2000 NHANES cross-sectional study
SAILOR: perceptual anchoring for robotic cognitive architectures
AbstractSymbolic anchoring is an important topic in robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors and maintain the link between that knowledge and the sensory data. In cognitive-based robots, this process of transforming sub-symbolic data generated by sensors to obtain and maintain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for symbolic anchoring integrated into ROS 2. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper describes the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2) and the validation of SAILOR using public datasets and a real-world scenario.
Lossless and reference-free compression of FASTQ/A files using GeneSqueeze
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
AbstractDeep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has improved all segmentation accuracy and structure volume/density evaluation metrics compared to a shallower baseline model with a smaller training database (N = 20). The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs ≥ .95) in detecting inaccurate and failed segmentations. Furthermore, the study has shown an impact of the disease severity status on the model’s predictive uncertainties when applied to a large-scale database. The high segmentation and muscle volume/density estimation accuracy and the high accuracy in failure detection based on the predictive uncertainty exhibited the model’s reliability for analyzing individual MSK structures in large-scale CT databases.
Clinical characteristics and prognosis of dialysis patients with metastatic calcification diagnosed by 99mTc-MDP bone scintigraphy
Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine
Genetic analysis and association detection of agronomic traits in maize genotypes
Amelioration of the growth and physiological responses of Capsicum annum L. via quantum dot-graphene oxide, cerium oxide, and titanium oxide nanoparticles foliar application under salinity stress
Fascicle differentiation of upper extremity nerves on high-resolution ultrasound with multimodal microscopic verification
AbstractThis study aimed to compare the fascicular anatomy of upper limb nerves visualized using in situ high-resolution ultrasound (HRUS) with ex vivo imaging modalities, namely, magnetic resonance microscopy (MRM), histological cross-sections (HCS), and optical projection tomography (OPT). The median, ulnar, and superficial branch of radial nerve (n = 41) were visualized in 14 cadaveric upper limbs using 22-MHz HRUS. Subsequently, the nerves were excised, imaged with different microscopic techniques, and their morphometric properties were compared. HRUS accurately differentiated 51–74% of fascicles, while MRM detected 87–92% of fascicles when compared to the referential HCS. Among the compared modalities, HRUS demonstrated the smallest fascicular ratios and fascicular cross-sectional areas, but the largest nerve cross-sectional areas. The probability of a fascicle depicted on HRUS representing a cluster of multiple fascicles on the referential HCS increased with the fascicular size, with some differences observed between the larger median and ulnar nerves and the smaller radial nerves. Accordingly, HRUS fascicle differentiation necessitates cautious interpretation, as larger fascicles are more likely to represent clusters. Although HCS is considered the reference modality, alterations in nerve cross-sectional areas or roundness during sample processing should be acknowledged.