Background: Exposure to air pollutants, including heavy metals, is a major environmental concern of public health and these environmental toxicants have been associated with pregnancy complications. Objectives: An air...
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With the advent of deep-learning based MR image reconstruction, new questions are raised regarding best design choices. In particular, when using recent reconstruction models that have been developed for processing mu...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
With the advent of deep-learning based MR image reconstruction, new questions are raised regarding best design choices. In particular, when using recent reconstruction models that have been developed for processing multi-channel MR data. A fundamental question is whether to first combine channels to improve model generalizability (i.e., a “coil-combined” approach) or to keep channel processing separate to fully utilize multi-channel information (i.e., an “all-coil” approach). In this work, we compare three popular architectures using coil-combined and all-coil styles on brain MR images. All-coil styles improved in-distribution performance, such as when reconstructing only presumed healthy individu-als. Coil-combined designs better generalized to unseen data from patients with pathology.
White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the per...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.
Information technology is introduced to agriculture in order to improve all the cultivating process and the quality of the products. In this article, we propose a method to organize the cultivated knowledge with a foc...
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A partial periodic pattern is referred to as a set of events that exhibits cyclic behavior over some periods in a time series. Previous studies focused on mining such patterns from constant-length segments that are al...
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The context-awareness is a central aspect in the design of pervasive systems, characterizing their ability to adapt its structure and behavior. The context-aware exception handling (CAEH) is an existing approach emplo...
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In the current landscape, security challenges extend to various domains, such as IoT networking, UAV communication, and VANETs, rendering underlying infrastructures increasingly susceptible to cyber-attacks. The taxon...
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This paper presents the design and construction of a low-cost combat robot named Artemis, developed for participation in the Robocore Experience 2024 competition. The project emphasizes the integration of cost-effecti...
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Customers in the banking industry nowadays have many options when deciding where to invest their money. Customer retention and churn have thus emerged as crucial challenges for the majority of banks. This research tri...
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Information technology is introduced to agriculture in order to improve all the cultivating process and the quality of the products. In this article, we propose a method to organize the cultivated knowledge with a foc...
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Information technology is introduced to agriculture in order to improve all the cultivating process and the quality of the products. In this article, we propose a method to organize the cultivated knowledge with a focus on the life cycle process, based on the concept of lightweight ontology. We also propose a method for acquiring the knowledge easily by applying the technology of softwareengineering. We interviewed the farmers cultivating high quality mandarin orange and organized the ontology using the proposed method, and show the effectiveness of the proposed method.
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