Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We ...
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Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad
High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of pro...
High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.
The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in ...
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ISBN:
(数字)9781728109626
ISBN:
(纸本)9781728109633
The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in public space for purposes like surveillance, monitoring, package delivery, emergency services, etc. For proper operation, an efficient path planning among IoD is required so that they can adaptively decide their path for data dissemination. Most of the existing solutions for this problem have made unreasonable assumptions and do not offer scalability. The scheme proposed in the paper provides a network architecture for the scalable solution of UAVs in an urban environment addressing issues of path planning, safety, privacy, and network connectivity. The scheme has been tested using exhaustive simulation and results prove that the proposed scheme is efficient in terms of reducing the overall cost and delivery time with the increasing weight of payload in the drones.
This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is ...
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This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of he...
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Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not ref...
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This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes fu...
This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.
In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture ...
Deep learning techniques hold promise to improve dense topography reconstruction and pose estimation, as well as simultaneous localization and mapping (SLAM). However, currently available datasets do not support effec...
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