| Using deep learning to segment breast and fibroglandular tissue in MRI volumes MU Dalmış, G Litjens, K Holland, A Setio, R Mann, N Karssemeijer, ... Medical physics 44 (2), 533-546, 2017 | 249 | 2017 |
| Artificial intelligence–based classification of breast lesions imaged with a multiparametric breast MRI protocol with ultrafast DCE-MRI, T2, and DWI MU Dalmiş, A Gubern-Mérida, S Vreemann, P Bult, N Karssemeijer, ... Investigative radiology 54 (6), 325-332, 2019 | 116 | 2019 |
| Fully automated detection of breast cancer in screening MRI using convolutional neural networks MU Dalmış, S Vreemann, T Kooi, RM Mann, N Karssemeijer, ... Journal of Medical Imaging 5 (1), 014502-014502, 2018 | 105 | 2018 |
| Interval cancer detection using a neural network and breast density in women with negative screening mammograms AJT Wanders, W Mees, PAM Bun, N Janssen, A Rodríguez-Ruiz, ... Radiology 303 (2), 269-275, 2022 | 48 | 2022 |
| A computer‐aided diagnosis system for breast DCE‐MRI at high spatiotemporal resolution MU Dalmış, A Gubern‐Mérida, S Vreemann, N Karssemeijer, R Mann, ... Medical physics 43 (1), 84-94, 2016 | 43 | 2016 |
| Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program: A … S Vreemann, MU Dalmis, P Bult, N Karssemeijer, MJM Broeders, ... European Radiology 29, 4678-4690, 2019 | 31 | 2019 |
| 3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging T Mertzanidou, JH Hipwell, S Reis, DJ Hawkes, B Ehteshami Bejnordi, ... Medical physics 44 (3), 935-948, 2017 | 29 | 2017 |
| Textural analysis of early‐phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE‐MRI protocol J Milenković, MU Dalmış, J Žgajnar, B Platel Medical physics 44 (9), 4652-4664, 2017 | 18 | 2017 |
| Similarity analysis of functional connectivity with functional near-infrared spectroscopy MU Dalmış, A Akın Journal of Biomedical Optics 20 (8), 086012, 2015 | 11 | 2015 |
| A fully automated system for quantification of background parenchymal enhancement in breast DCE-MRI MU Dalmiş, A Gubern-Mérida, C Borelli, S Vreemann, RM Mann, ... Medical Imaging 2016: Computer-Aided Diagnosis 9785, 146-152, 2016 | 8 | 2016 |
| Towards spatial correspondence between specimen and in-vivo breast imaging T Mertzanidou, J Hipwell, M Dalmis, B Platel, J van der Laak, R Mann, ... Breast Imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan …, 2014 | 5 | 2014 |
| Whole mastectomy volume reconstruction from 2d radiographs and its mapping to histology T Mertzanidou, JH Hipwell, S Reis, BE Bejnordi, M Hermsen, M Dalmis, ... Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June …, 2016 | 4 | 2016 |
| Apparatus for determining a temporal breast cancer risk N Karssemeijer, M Dalmis, A Rodriguez, N Jansen, M Kallenberg, ... US Patent App. 18/278,056, 2024 | | 2024 |
| Automated analysis of breast MRI: From Traditional Methods into Deep Learning MU Dalmis | | 2019 |
| Similarity analysis of functional connectivity with functional near-infrared spectroscopy A Akın, MU Dalmış Spie-Soc Photo-Optical Instrumentation Engineers, 2015 | | 2015 |
| Similarity and consistency analysis of functional connectivity maps MU Dalmiş Biyo-Medikal Mühendislik Enstitüsü, 2013 | | 2013 |
Nico KarssemeijerProfessor of Computer Aided Diagnosis, Radboud University NijmegenAdresă de e-mail confirmată pe radboudumc.nl