Zobrazeno 1 - 10
of 341
pro vyhledávání: '"Nyholm Tufve"'
Publikováno v:
Radiology and Oncology, Vol 52, Iss 2, Pp 143-151 (2018)
The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiothe
Externí odkaz:
https://doaj.org/article/2375c99cca6a40eea2cbf9f4ff105d09
Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducib
Externí odkaz:
http://arxiv.org/abs/2210.11146
Autor:
Kaushik, Sandeep, Bylund, Mikael, Cozzini, Cristina, Shanbhag, Dattesh, Petit, Steven F, Wyatt, Jonathan J, Menzel, Marion I, Pirkl, Carolin, Mehta, Bhairav, Chauhan, Vikas, Chandrasekharan, Kesavadas, Jonsson, Joakim, Nyholm, Tufve, Wiesinger, Florian, Menze, Bjoern
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss f
Externí odkaz:
http://arxiv.org/abs/2203.16288
Autor:
Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 1 (2022)
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e
Externí odkaz:
http://arxiv.org/abs/2112.10074
Autor:
Grefve, Josefine, Söderkvist, Karin, Gunnlaugsson, Adalsteinn, Sandgren, Kristina, Jonsson, Joakim, Keeratijarut Lindberg, Angsana, Nilsson, Erik, Axelsson, Jan, Bergh, Anders, Zackrisson, Björn, Moreau, Mathieu, Thellenberg Karlsson, Camilla, Olsson, Lars.E., Widmark, Anders, Riklund, Katrine, Blomqvist, Lennart, Berg Loegager, Vibeke, Strandberg, Sara N., Nyholm, Tufve
Publikováno v:
In Physics and Imaging in Radiation Oncology July 2024 31
Autor:
Simkó, Attila, Bylund, Mikael, Jönsson, Gustav, Löfstedt, Tommy, Garpebring, Anders, Nyholm, Tufve, Jonsson, Joakim
Publikováno v:
In Zeitschrift fuer Medizinische Physik May 2024 34(2):270-277
In the last years, deep learning has dramatically improved the performances in a variety of medical image analysis applications. Among different types of deep learning models, convolutional neural networks have been among the most successful and they
Externí odkaz:
http://arxiv.org/abs/2104.11020
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice. Unfortunately, manual segmentation is very challenging, time-consuming, costly, and often inaccurate despite human expertise due to the
Externí odkaz:
http://arxiv.org/abs/2012.03684
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns re
Externí odkaz:
http://arxiv.org/abs/2003.08760
When using Convolutional Neural Networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice (2D) or whole volumes (3D). One common alternative, in this
Externí odkaz:
http://arxiv.org/abs/1912.09287