This article proposes a novel community detection approach, MHNMF, which analyzes the multihop connectivity patterns within the network. We subsequently proceed to derive an algorithm that efficiently optimizes MHNMF, along with a comprehensive theoretical analysis of its computational complexity and convergence. Twelve real-world benchmark networks were used to empirically compare MHNMF against 12 state-of-the-art community detection methods, demonstrating the superior performance of MHNMF.
Inspired by the human visual system's global-local processing, we propose a novel convolutional neural network (CNN), CogNet, which comprises a global pathway, a local pathway, and a top-down modulation mechanism. The local pathway, designed to extract intricate local details of the input image, is initially constructed by using a universal CNN block. To form the global pathway, capturing global structural and contextual information among local image parts, we employ a transformer encoder. In the final step, we design the learnable top-down modulator, utilizing global representations of the global pathway to refine the intricate local features of the local pathway. In the interest of ease of use, the dual-pathway computation and modulation process is packaged into a component, the global-local block (GL block). A CogNet of any depth can be developed by stacking a predetermined number of GL blocks. Empirical analysis of CogNets across six standard datasets confirms their superior accuracy, exceeding current state-of-the-art results and effectively mitigating texture and semantic confusion prevalent in CNN models.
Walking-related human joint torques are frequently determined through the application of inverse dynamics. Traditional analysis strategies depend on preliminary ground reaction force and kinematic measurements. This research introduces a novel real-time hybrid approach, combining a neural network and a dynamic model, which necessitates only kinematic data. From kinematic data, an end-to-end neural network is designed and developed for the precise estimation of joint torques directly. Neural networks are educated on diverse walking conditions, including the start and stop sequences, sudden alterations in pace, and the distinctive characteristic of asymmetrical movement. The initial testing of the hybrid model involves a comprehensive dynamic gait simulation (OpenSim), producing root mean square errors below 5 N.m and a correlation coefficient above 0.95 for each joint. In experimental trials, the end-to-end model frequently achieves superior performance compared to the hybrid model throughout the testing set, as assessed against the gold standard method, demanding both kinetic and kinematic considerations. To further evaluate the two torque estimators, a participant wearing a lower limb exoskeleton was included in the testing. This instance showcases the hybrid model (R>084) performing considerably better than the end-to-end neural network (R>059). embryo culture medium Differing situations, not present in the training data, benefit from the hybrid model's application.
Blood vessel thromboembolism, if left unchecked, can result in stroke, heart attack, and ultimately, sudden death. Ultrasound contrast agents, when combined with sonothrombolysis, have effectively treated thromboembolism, showing encouraging results. With the recent introduction of intravascular sonothrombolysis, there is a potential for a safe and effective approach to addressing deep vein thrombosis. Despite the positive results observed in the treatment, the efficiency for clinical application may not be maximized in the absence of imaging guidance and clot characterization throughout the thrombolysis procedure. This study details the design of a miniaturized transducer for intravascular sonothrombolysis. The transducer is an 8-layer PZT-5A stack with a 14×14 mm² aperture, housed within a custom-fabricated 10-Fr two-lumen catheter. II-PAT, a hybrid imaging modality, monitored the treatment, leveraging the distinctive contrast from optical absorption and the extensive depth of ultrasound detection. Using a thin optical fiber integrated into an intravascular catheter for light delivery, II-PAT's method effectively overcomes the depth limitations due to the substantial optical attenuation within tissues. In-vitro studies employing PAT-guided sonothrombolysis were performed on synthetic blood clots embedded within a tissue-mimicking phantom. II-PAT estimates clot position, shape, stiffness, and oxygenation level at a clinically relevant depth of ten centimeters. GDC-0941 supplier Through the use of real-time feedback during the procedure, the feasibility of PAT-guided intravascular sonothrombolysis has been substantiated by our research.
Under dual-energy spectral CT (DECT), a novel computer-aided diagnosis (CADx) framework, designated CADxDE, was formulated in this study. This framework directly utilizes pre-log domain transmission data for spectral analysis to aid in lesion diagnosis. The CADxDE encompasses material identification, along with machine learning (ML) based CADx. DECT's virtual monoenergetic imaging technology, applied to identified materials, allows for machine learning analysis of diverse tissue responses (including muscle, water, and fat) in lesions at different energy levels, which is crucial for computer-aided diagnosis. Employing an iterative reconstruction technique, rooted in a pre-log domain model, the DECT scan's essential details are preserved while generating decomposed material images. These images are subsequently used to create virtual monoenergetic images (VMIs) at selected n energies. Despite sharing the same underlying anatomical layout, the contrast distribution patterns of these VMIs, accompanied by the n-energies, hold substantial implications for tissue characterization. For this purpose, an ML-based CADx system is constructed to take advantage of the energy-heightened tissue attributes for the purpose of identifying malignant and benign lesions. neurology (drugs and medicines) To demonstrate the potential of CADxDE, an original image-based multi-channel 3D convolutional neural network (CNN) and extracted lesion feature-driven machine learning computer-aided diagnostic (CADx) methods are created. In three pathologically confirmed clinical datasets, AUC scores were 401% to 1425% higher than those from both high- and low-energy DECT data and conventional CT data. The diagnostic performance of lesions saw a substantial boost, exceeding 913% in the mean AUC scores, thanks to the energy spectral-enhanced tissue features from CADxDE.
In computational pathology, whole-slide image (WSI) classification is indispensable, yet proves challenging due to extra-high resolution, the expensive and time-consuming process of manual annotation, and the variations in data heterogeneity. Multiple instance learning (MIL) presents a promising path for classifying whole-slide images (WSIs), but the gigapixel resolution inherently creates a memory bottleneck. For this reason, the majority of existing MIL approaches necessitate the detachment of the feature encoder from the MIL aggregator, which can have a significant adverse impact on the outcome. This paper introduces a Bayesian Collaborative Learning (BCL) approach to resolve the memory constraint in the context of WSI classification. The introduction of an auxiliary patch classifier allows for interactive learning with the target MIL classifier, enabling cooperative learning of the feature encoder and the MIL aggregator components within the MIL classifier. This approach effectively addresses the memory bottleneck. Under the umbrella of a unified Bayesian probabilistic framework, a collaborative learning procedure is devised, incorporating a principled Expectation-Maximization algorithm to infer optimal model parameters iteratively. For an effective implementation of the E-step, a pseudo-labeling method that considers quality is also presented. A comprehensive assessment of the proposed BCL was conducted utilizing three publicly available whole slide image datasets: CAMELYON16, TCGA-NSCLC, and TCGA-RCC. The resulting AUC values of 956%, 960%, and 975%, respectively, highlight significant performance improvements over existing methods. To ensure an extensive comprehension, a comprehensive analysis coupled with a detailed discussion of the method will be given. To foster further development, our source code is publicly available on Github at https://github.com/Zero-We/BCL.
Anatomical representation of head and neck vessels serves as a pivotal diagnostic step in cerebrovascular disease evaluation. Accurate automated labeling of vessels in computed tomography angiography (CTA) remains challenging, especially in the head and neck, due to the intricate branching and tortuous configuration of the vessels, which are often situated in close proximity to adjacent vascular structures. In the effort to resolve these impediments, a novel topology-alerting graph network, termed TaG-Net, is put forward for vessel labeling. It elegantly combines volumetric image segmentation in voxel space with centerline labeling in line space, allowing for precise local feature identification in the voxel domain and higher-level anatomical and topological information for vessels via the vascular graph derived from centerlines. The process begins with extracting centerlines from the initial vessel segmentation, culminating in the creation of a vascular graph. To label the vascular graph, we then employ TaG-Net, combining topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graphs. Following this, the vascular graph, marked with labels, is used to enhance volumetric segmentation by completing vessel structures. After all steps, the head and neck vessels in 18 segments are labeled by assigning centerline labels to the refined segmentation process. In experiments involving 401 subjects' CTA images, our technique achieved superior vessel segmentation and labeling performance relative to other current best-practice methods.
The potential for real-time performance is driving increased interest in regression-based multi-person pose estimation techniques.