Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Mohammad Farid Azampour"'
Autor:
Mohammed Yusuf Ansari, Yin Yang, Shidin Balakrishnan, Julien Abinahed, Abdulla Al-Ansari, Mohamed Warfa, Omran Almokdad, Ali Barah, Ahmed Omer, Ajay Vikram Singh, Pramod Kumar Meher, Jolly Bhadra, Osama Halabi, Mohammad Farid Azampour, Nassir Navab, Thomas Wendler, Sarada Prasad Dakua
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a f
Externí odkaz:
https://doaj.org/article/11f190f50a7140a3a1f9f5c217b96361
Autor:
Mohammed Yusuf Ansari, Yin Yang, Shidin Balakrishnan, Julien Abinahed, Abdulla Al-Ansari, Mohamed Warfa, Omran Almokdad, Ali Barah, Ahmed Omer, Ajay Vikram Singh, Pramod Kumar Meher, Jolly Bhadra, Osama Halabi, Mohammad Farid Azampour, Nassir Navab, Thomas Wendler, Sarada Prasad Dakua
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-1 (2022)
Externí odkaz:
https://doaj.org/article/b5c79c607a794cfd9efc2708c73cc58b
Autor:
Daniel Grzech, Mohammad Farid Azampour, Huaqi Qiu, Ben Glocker, Bernhard Kainz, Loïc Le Folgoc
Publikováno v:
Machine Learning for Biomedical Imaging. 1:1-25
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computa
Autor:
Daniel Grzech, Mohammad Farid Azampour, Ben Glocker, Julia Schnabel, Nassir Navab, Bernhard Kainz, Loic Le Folgoc
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Autor:
Bailiang Jian, Mohammad Farid Azampour, Francesca De Benetti, Johannes Oberreuter, Christina Bukas, Alexandra S. Gersing, Sarah C. Foreman, Anna-Sophia Dietrich, Jon Rischewski, Jan S. Kirschke, Nassir Navab, Thomas Wendler
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164453
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b265bd9aa03d1b1c48e3f0991dd805e
https://doi.org/10.1007/978-3-031-16446-0_22
https://doi.org/10.1007/978-3-031-16446-0_22
Autor:
Yordanka Velikova, Walter Simson, Mehrdad Salehi, Mohammad Farid Azampour, Philipp Paprottka, Nassir Navab
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164361
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::287e17e370434ef38b6152e9a08ada19
https://doi.org/10.1007/978-3-031-16437-8_47
https://doi.org/10.1007/978-3-031-16437-8_47
Autor:
Adeleh Bitarafan, Mohammad Farid Azampour, Kian Bakhtari, Mahdieh Soleymani Baghshah, Matthias Keicher, Nassir Navab
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164392
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::069982a337ed4c593c4c86874119c9b0
https://doi.org/10.1007/978-3-031-16440-8_58
https://doi.org/10.1007/978-3-031-16440-8_58
Autor:
Magdalini Paschali, Christine Eilers, Maria Tirindelli, Nassir Navab, Walter Simson, Mohammad Farid Azampour
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872366
MICCAI (8)
MICCAI (8)
Medical Ultrasound (US), despite its wide use, is characterized by artefacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of deep neural networks used for computer-assisted interventio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40c7fc40257cd4ac427c6eabaa6e83b4
https://doi.org/10.1007/978-3-030-87237-3_66
https://doi.org/10.1007/978-3-030-87237-3_66
Autor:
Emad Fatemizadeh, Magdalini Paschali, Nassir Navab, Mohammad Farid Azampour, Maria Tirindelli, Hannes Hase, Walter Simson
Publikováno v:
IROS
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory b
Publikováno v:
EMBC
Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structu