Zobrazeno 1 - 10
of 676
pro vyhledávání: '"Karssemeijer, Nico"'
Autor:
Moriakov, Nikita, Peters, Jim, Mann, Ritse, Karssemeijer, Nico, van Dijck, Jos, Broeders, Mireille, Teuwen, Jonas
Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammograms, which
Externí odkaz:
http://arxiv.org/abs/2308.14369
Autor:
Lauritzen, Andreas D., von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, Ilse, Nielsen, Mads, Karssemeijer, Nico, Lillholm, Martin
Purpose: Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, asso
Externí odkaz:
http://arxiv.org/abs/2212.13439
Autor:
Moriakov, Nikita, Peters, Jim, Mann, Ritse, Karssemeijer, Nico, van Dijck, Jos, Broeders, Mireille, Teuwen, Jonas
Publikováno v:
In Medical Image Analysis October 2024 97
Akademický článek
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Autor:
Tellez, David, Balkenhol, Maschenka, Otte-Holler, Irene, van de Loo, Rob, Vogels, Rob, Bult, Peter, Wauters, Carla, Vreuls, Willem, Mol, Suzanne, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen, Ciompi, Francesco
Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures
Externí odkaz:
http://arxiv.org/abs/1808.05896
Autor:
Ghafoorian, Mohsen, Teuwen, Jonas, Manniesing, Rashindra, de Leeuw, Frank-Erik, van Ginneken, Bram, Karssemeijer, Nico, Platel, Bram
Publikováno v:
Proc. SPIE 10574, 105742U (2 March 2018)
Ventricular volume and its progression are known to be linked to several brain diseases such as dementia and schizophrenia. Therefore accurate measurement of ventricle volume is vital for longitudinal studies on these disorders, making automated vent
Externí odkaz:
http://arxiv.org/abs/1801.05040
Autor:
Bejnordi, Babak Ehteshami, Zuidhof, Guido, Balkenhol, Maschenka, Hermsen, Meyke, Bult, Peter, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for c
Externí odkaz:
http://arxiv.org/abs/1705.03678
Autor:
Kooi, Thijs, Karssemeijer, Nico
We investigate the addition of symmetry and temporal context information to a deep Convolutional Neural Network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the locatio
Externí odkaz:
http://arxiv.org/abs/1703.07715
Autor:
Ghafoorian, Mohsen, Mehrtash, Alireza, Kapur, Tina, Karssemeijer, Nico, Marchiori, Elena, Pesteie, Mehran, Guttmann, Charles R. G., de Leeuw, Frank-Erik, Tempany, Clare M., van Ginneken, Bram, Fedorov, Andriy, Abolmaesumi, Purang, Platel, Bram, Wells III, William M.
Publikováno v:
Medical Image Computing and Computer-Assisted Intervention 2017, Vol 10435, 516-524
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (
Externí odkaz:
http://arxiv.org/abs/1702.07841