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Autor:
Artênio Fonseca
Imagine-se numa aventura em que você faça dez mil viagens acompanhado de pessoas diferentes e lugares inusitados. Cada uma com sua história narrada, como que num divã. Mas essas pessoas seriam anônimas e nunca mais vocês as encontrariam. Detalh
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base
Externí odkaz:
http://arxiv.org/abs/2410.11651
The rapidly developing deep learning (DL) techniques have been applied in software systems with various application scenarios. However, they could also pose new safety threats with potentially serious consequences, especially in safety-critical domai
Externí odkaz:
http://arxiv.org/abs/2405.07744
The task of industrial detection based on deep learning often involves solving two problems: (1) obtaining sufficient and effective data samples, (2) and using efficient and convenient model training methods. In this paper, we introduce a novel defec
Externí odkaz:
http://arxiv.org/abs/2407.03332
Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long acquisition times and can compromise the clinical utility of acquired images. Traditional motion correction methods often fail to address severe motion, leading to dist
Externí odkaz:
http://arxiv.org/abs/2407.02974
Autor:
Dernedde, Tim, Thyssens, Daniela, Dittrich, Sören, Stubbemann, Maximilian, Schmidt-Thieme, Lars
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from
Externí odkaz:
http://arxiv.org/abs/2402.04915
Medical imaging data is often siloed within hospitals, limiting the amount of data available for specialized model development. With limited in-domain data, one might hope to leverage larger datasets from related domains. In this paper, we analyze th
Externí odkaz:
http://arxiv.org/abs/2311.09401
Publikováno v:
In Chemical Engineering Journal 15 November 2024 500
Akademický článek
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