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pro vyhledávání: '"Kilián, M."'
Autor:
Paschali, Magdalini, Jiang, Yu Hang, Siegel, Spencer, Gonzalez, Camila, Pohl, Kilian M., Chaudhari, Akshay, Zhao, Qingyu
Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease
Externí odkaz:
http://arxiv.org/abs/2410.00946
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
Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative
Externí odkaz:
http://arxiv.org/abs/2409.08463
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
Autor:
Kim, Soopil, An, Sion, Chikontwe, Philip, Kang, Myeongkyun, Adeli, Ehsan, Pohl, Kilian M., Park, Sang Hyun
Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various component types t
Externí odkaz:
http://arxiv.org/abs/2312.13783
Autor:
Peng, Wei, Bosschieter, Tomas, Ouyang, Jiahong, Paul, Robert, Adeli, Ehsan, Zhao, Qingyu, Pohl, Kilian M.
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI research only focuses on optimizing the visual quality (such as signal
Externí odkaz:
http://arxiv.org/abs/2310.04630
Publikováno v:
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates
Externí odkaz:
http://arxiv.org/abs/2310.00213
Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not rel
Externí odkaz:
http://arxiv.org/abs/2308.09907
One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could b
Externí odkaz:
http://arxiv.org/abs/2307.13108