Zobrazeno 1 - 10
of 8 911
pro vyhledávání: '"Adeli A"'
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
This research paper delves into the Linear Kalman Filter (LKF), highlighting its importance in merging data from multiple sensors. The Kalman Filter is known for its recursive solution to the linear filtering problem in discrete data, making it ideal
Externí odkaz:
http://arxiv.org/abs/2407.13062
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
Autor:
Rahmani, Amir Masoud, Haider, Amir, Adeli, Mohammad, Mzoughi, Olfa, Gemeay, Entesar, Mohammadi, Mokhtar, Alinejad-Rokny, Hamid, Khoshvaght, Parisa, Hosseinzadeh, Mehdi
This paper explores the efficacy of Mel Frequency Cepstral Coefficients (MFCCs) in detecting abnormal heart sounds using two classification strategies: a single classifier and an ensemble classifier approach. Heart sounds were first pre-processed to
Externí odkaz:
http://arxiv.org/abs/2406.00702
Autor:
Adeli, Vida, Mehraban, Soroush, Ballester, Irene, Zarghami, Yasamin, Sabo, Andrea, Iaboni, Andrea, Taati, Babak
Publikováno v:
IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)
This study investigates the application of general human motion encoders trained on large-scale human motion datasets for analyzing gait patterns in PD patients. Although these models have learned a wealth of human biomechanical knowledge, their effe
Externí odkaz:
http://arxiv.org/abs/2405.17817