Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Granziera Cristina"'
Autor:
Spagnolo, Federico, Molchanova, Nataliia, Pineda, Mario Ocampo, Melie-Garcia, Lester, Cuadra, Meritxell Bach, Granziera, Cristina, Andrearczyk, Vincent, Depeursinge, Adrien
To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenari
Externí odkaz:
http://arxiv.org/abs/2409.03772
Autor:
Molchanova, Nataliia, Cagol, Alessandro, Gordaliza, Pedro M., Ocampo-Pineda, Mario, Lu, Po-Jui, Weigel, Matthias, Chen, Xinjie, Depeursinge, Adrien, Granziera, Cristina, Müller, Henning, Cuadra, Meritxell Bach
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep lea
Externí odkaz:
http://arxiv.org/abs/2407.05761
Autor:
Spagnolo, Federico, Molchanova, Nataliia, Schaer, Roger, Cuadra, Meritxell Bach, Pineda, Mario Ocampo, Melie-Garcia, Lester, Granziera, Cristina, Andrearczyk, Vincent, Depeursinge, Adrien
In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of classification tasks. However, XAI for semantic segmentation and in particular for single instances ha
Externí odkaz:
http://arxiv.org/abs/2406.09335
Autor:
Durrer, Alicia, Wolleb, Julia, Bieder, Florentin, Friedrich, Paul, Melie-Garcia, Lester, Ocampo-Pineda, Mario, Bercea, Cosmin I., Hamamci, Ibrahim E., Wiestler, Benedikt, Piraud, Marie, Yaldizli, Özgür, Granziera, Cristina, Menze, Bjoern H., Cattin, Philippe C., Kofler, Florian
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for the evaluation of volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy
Externí odkaz:
http://arxiv.org/abs/2403.14499
Autor:
Molchanova, Nataliia, Raina, Vatsal, Malinin, Andrey, La Rosa, Francesco, Depeursinge, Adrien, Gales, Mark, Granziera, Cristina, Muller, Henning, Graziani, Mara, Cuadra, Meritxell Bach
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosi
Externí odkaz:
http://arxiv.org/abs/2311.08931
Autor:
Bédard, Sandrine, Karthik, Enamundram Naga, Tsagkas, Charidimos, Pravatà, Emanuele, Granziera, Cristina, Smith, Andrew, Weber II, Kenneth Arnold, Cohen-Adad, Julien
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi an
Externí odkaz:
http://arxiv.org/abs/2310.15402
Autor:
Lu, Po-Jui, Odry, Benjamin, Barakovic, Muhamed, Weigel, Matthias, Sandkühler, Robin, Rahmanzadeh, Reza, Chen, Xinjie, Ocampo-Pineda, Mario, Kuhle, Jens, Kappos, Ludwig, Cattin, Philippe, Granziera, Cristina
Objective: Identifying disability-related brain changes is important for multiple sclerosis (MS) patients. Currently, there is no clear understanding about which pathological features drive disability in single MS patients. In this work, we propose a
Externí odkaz:
http://arxiv.org/abs/2308.07611
Autor:
Barakovic, Muhamed, Pizzolato, Marco, Tax, Chantal M. W., Rudrapatna, Umesh, Magon, Stefano, Dyrby, Tim B., Granziera, Cristina, Thiran, Jean-Philippe, Jones, Derek K., Canales-Rodríguez, Erick J.
Publikováno v:
Frontiers in Neuroscience, Vol 17, 2023. https://www.frontiersin.org/articles/10.3389/fnins.2023.1209521
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the ra
Externí odkaz:
http://arxiv.org/abs/2304.09275
Autor:
Durrer, Alicia, Wolleb, Julia, Bieder, Florentin, Sinnecker, Tim, Weigel, Matthias, Sandkühler, Robin, Granziera, Cristina, Yaldizli, Özgür, Cattin, Philippe C.
Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longitudinal comparability is essential but can be impaired by these contra
Externí odkaz:
http://arxiv.org/abs/2303.08189
Autor:
Molchanova, Nataliia, Raina, Vatsal, Malinin, Andrey, La Rosa, Francesco, Muller, Henning, Gales, Mark, Granziera, Cristina, Graziani, Mara, Cuadra, Meritxell Bach
Publikováno v:
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-s
Externí odkaz:
http://arxiv.org/abs/2211.04825