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1.
Alkhodari, M.* et al.: Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images. Comput. Meth. Programs Biomed. 248:108107 (2024)
2.
Lamm, L. et al.: MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms. Comput. Meth. Programs Biomed. 224:106990 (2022)
3.
van Veen, R.* et al.: FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. Comput. Meth. Programs Biomed. 225:107042 (2022)
4.
Sippel, K. et al.: Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data. Comput. Meth. Programs Biomed. 173, 35-41 (2019)
5.
Klutke, P.J. ; Mattioli, P.* ; Baruffaldi, F.* ; Toni, A.* & Englmeier, K.-H.: The telemedicine benchmark: a general tool to measure and compare the performance of videoconferencing equipment in the telemedicine area. Comput. Meth. Programs Biomed. 60, 133-141 (1999)
6.
Mattioli, P.* et al.: A study of the application sharing capabilities in telemedicine. Comput. Meth. Programs Biomed. 58, 89-97 (1999)
7.
Mattioli, P.* et al.: Technical validation of low-cost videoconferencing systems applied in orthopaedric teleconsulting services. Comput. Meth. Programs Biomed. 60, 143-152 (1999)
8.
Engelbrecht, R. et al.: A chip card for patients with diabetes. Comput. Meth. Programs Biomed. 45, 33-35 (1994)
9.
Lipinski, H.G. & Kuether, G.: Graphical visualization of the pattern of muscular weakness in neuromuscular diseases. Comput. Meth. Programs Biomed. 34, 69-73 (1991)
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