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pro vyhledávání: '"A. Adeli"'
Rocky planets in our Solar System, namely Mercury, Venus, Earth, Mars, and the Moon, which is generally added to this group due to its geological complexity, possess a solid surface and share a common structure divided into major layers, namely a sil
Externí odkaz:
http://arxiv.org/abs/2411.10577
Autor:
Wang, Yanchen, Turnbull, Adam, Xiang, Tiange, Xu, Yunlong, Zhou, Sa, Masoud, Adnan, Azizi, Shekoofeh, Lin, Feng Vankee, Adeli, Ehsan
Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advancements in functional Magnetic Resonance Imaging and machine lear
Externí odkaz:
http://arxiv.org/abs/2411.07121
Autor:
He, Shizhe, Paschali, Magdalini, Ouyang, Jiahong, Masood, Adnan, Chaudhari, Akshay, Adeli, Ehsan
Publikováno v:
International Workshop on Machine Learning in Clinical Neuroimaging (MLCN) 2024
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream tasks. This approach is particularly valuable in leveraging the structural
Externí odkaz:
http://arxiv.org/abs/2410.12053
The rapid advancement of medical technology has led to an exponential increase in multi-modal medical data, including imaging, genomics, and electronic health records (EHRs). Graph neural networks (GNNs) have been widely used to represent this data d
Externí odkaz:
http://arxiv.org/abs/2410.00944
Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in
Externí odkaz:
http://arxiv.org/abs/2410.07201
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship
Externí odkaz:
http://arxiv.org/abs/2409.13887
Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To
Externí odkaz:
http://arxiv.org/abs/2409.13532
Autor:
Peng, Wei, Xia, Tian, Ribeiro, Fabio De Sousa, Bosschieter, Tomas, Adeli, Ehsan, Zhao, Qingyu, Glocker, Ben, Pohl, Kilian M.
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. H
Externí odkaz:
http://arxiv.org/abs/2409.05585
Most existing human rendering methods require every part of the human to be fully visible throughout the input video. However, this assumption does not hold in real-life settings where obstructions are common, resulting in only partial visibility of
Externí odkaz:
http://arxiv.org/abs/2407.00316
Autor:
Durante, Zane, Harries, Robathan, Vendrow, Edward, Luo, Zelun, Kyuragi, Yuta, Kozuka, Kazuki, Fei-Fei, Li, Adeli, Ehsan
Understanding Activities of Daily Living (ADLs) is a crucial step for different applications including assistive robots, smart homes, and healthcare. However, to date, few benchmarks and methods have focused on complex ADLs, especially those involvin
Externí odkaz:
http://arxiv.org/abs/2406.01662