Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Jakoba"'
Autor:
Jakoba J. Eertink, Idris Bahce, John C. Waterton, Marc C. Huisman, Ronald Boellaard, Andreas Wunder, Andrea Thiele, Catharina W. Menke-van der Houven van Oordt
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, rel
Externí odkaz:
https://doaj.org/article/b1a23010f6ce4bedaedce8f56d996948
Autor:
Maria C. Ferrández, Sandeep S. V. Golla, Jakoba J. Eertink, Bart M. de Vries, Sanne E. Wiegers, Gerben J. C. Zwezerijnen, Simone Pieplenbosch, Louise Schilder, Martijn W. Heymans, Josée M. Zijlstra, Ronald Boellaard
Publikováno v:
EJNMMI Research, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Background Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to
Externí odkaz:
https://doaj.org/article/cbda86c527ec46e18d80e8f0c03767b8
Autor:
Maria C. Ferrández, Sandeep S. V. Golla, Jakoba J. Eertink, Bart M. de Vries, Pieternella J. Lugtenburg, Sanne E. Wiegers, Gerben J. C. Zwezerijnen, Simone Pieplenbosch, Lars Kurch, Andreas Hüttmann, Christine Hanoun, Ulrich Dührsen, Henrica C. W. de Vet, PETRA, Josée M. Zijlstra, Ronald Boellaard
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorod
Externí odkaz:
https://doaj.org/article/3b314a83aa2940e6ae107cea2b76e66d
Autor:
Jakoba J. Eertink, Martijn W. Heymans, Gerben J. C. Zwezerijnen, Josée M. Zijlstra, Henrica C. W. de Vet, Ronald Boellaard
Publikováno v:
EJNMMI Research, Vol 12, Iss 1, Pp 1-8 (2022)
Abstract Aim Clinical prediction models need to be validated. In this study, we used simulation data to compare various internal and external validation approaches to validate models. Methods Data of 500 patients were simulated using distributions of
Externí odkaz:
https://doaj.org/article/4455ff8241f04b09a4b0b5f5789a25b8
Autor:
Esther E. E. Drees, Julia Driessen, Gerben J. C. Zwezerijnen, Sandra A. W. M. Verkuijlen, Jakoba J. Eertink, Monique A. J. vanEijndhoven, Nils J. Groenewegen, Andrea Vallés‐Martí, Daphne deJong, Ronald Boellaard, Henrica C. W. deVet, Dirk M. Pegtel, Josée M. Zijlstra
Publikováno v:
eJHaem, Vol 3, Iss 3, Pp 908-912 (2022)
Abstract Blood‐based biomarkers are gaining interest for response evaluation in classical Hodgkin lymphoma (cHL). However, it is unknown how blood‐based biomarkers relate to quantitative 18F‐FDG‐PET features. We correlated extracellular vesic
Externí odkaz:
https://doaj.org/article/8a2200b23a984d21aa0f98dc54ac8e5b
Autor:
Maria C. Ferrández, Jakoba J. Eertink, Sandeep S. V. Golla, Sanne E. Wiegers, Gerben J. C. Zwezerijnen, Simone Pieplenbosch, Josée M. Zijlstra, Ronald Boellaard
Publikováno v:
EJNMMI Research, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction m
Externí odkaz:
https://doaj.org/article/cc1634d50ca343e2a842fb286dbb2c65
Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the precision of the accumulator can further improve hardware efficiency at
Externí odkaz:
http://arxiv.org/abs/2401.10432
We present accumulator-aware quantization (A2Q), a novel weight quantization method designed to train quantized neural networks (QNNs) to avoid overflow when using low-precision accumulators during inference. A2Q introduces a unique formulation inspi
Externí odkaz:
http://arxiv.org/abs/2308.13504
We introduce a quantization-aware training algorithm that guarantees avoiding numerical overflow when reducing the precision of accumulators during inference. We leverage weight normalization as a means of constraining parameters during training usin
Externí odkaz:
http://arxiv.org/abs/2301.13376
Autor:
Driessen, Julia, Zwezerijnen, Gerben J. C., Schöder, Heiko, Kersten, Marie José, Moskowitz, Alison J., Moskowitz, Craig H., Eertink, Jakoba J., Heymans, Martijn W., Boellaard, Ronald, Zijlstra, Josée M.
Publikováno v:
In Blood Advances 14 November 2023 7(21):6732-6743