Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health learning; machine monitoring networks; neural rotors; structural]
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, July 2023. [PUMA: topic_visualcomputing graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]
Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: topic_visualcomputing XAI, explainability, learning, machine trust] URL
Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, January 2023. [PUMA: topic_neuroinspired Dementia; Diagnosis; MRI; Machine Neurodegeneration; Volumetry learning; topic_lifescience unit_test]
CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome Med., (15)1:61, August 2023. [PUMA: topic_lifescience Cell Immunocompromised; Machine Macrophages; Myocardial Single-cell infarction; learning; therapy;]