| A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease S Spasov, L Passamonti, A Duggento, P Lio, N Toschi, ... Neuroimage 189, 276-287, 2019 | 392* | 2019 |
| A multi-modal convolutional neural network framework for the prediction of Alzheimer’s disease SE Spasov, L Passamonti, A Duggento, P Lio, N Toschi 2018 40th annual international conference of the IEEE engineering in …, 2018 | 84 | 2018 |
| Multimodal and multicontrast image fusion via deep generative models GM Dimitri, S Spasov, A Duggento, L Passamonti, P Lió, N Toschi Information Fusion 88, 146-160, 2022 | 28 | 2022 |
| Integration of machine learning methods to dissect genetically imputed transcriptomic profiles in Alzheimer’s disease C Maj, T Azevedo, V Giansanti, O Borisov, GM Dimitri, S Spasov, ... Frontiers in genetics 10, 726, 2019 | 28 | 2019 |
| OptiJ: Open-source optical projection tomography of large organ samples PP Vallejo Ramirez, J Zammit, O Vanderpoorten, F Riche, FX Blé, ... Scientific reports 9 (1), 15693, 2019 | 27 | 2019 |
| Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson’s disease patients E Zago, A Dal Molin, GM Dimitri, L Xumerle, C Pirazzini, MG Bacalini, ... Scientific reports 12 (1), 1330, 2022 | 23 | 2022 |
| Unsupervised stratification in neuroimaging through deep latent embeddings GM Dimitri, S Spasov, A Duggento, L Passamonti, N Toschi 2020 42nd Annual International Conference of the IEEE Engineering in …, 2020 | 21 | 2020 |
| A geroscience approach for Parkinson’s disease: Conceptual framework and design of PROPAG-AGEING project C Pirazzini, T Azevedo, L Baldelli, A Bartoletti-Stella, ... Mechanisms of Ageing and Development 194, 111426, 2021 | 17 | 2021 |
| Dynamic Neural Network Channel Execution for Efficient Training SE Spasov, P Liò British Machine Vision Conference (BMVC), 2019 | 7 | 2019 |
| Grade: Graph dynamic embedding S Spasov, A Di Stefano, P Lio, J Tang arXiv preprint arXiv:2007.08060, 2020 | 6 | 2020 |
| RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow S Glass, S Spasov, P Liò 7th ICML Workshop on Automated Machine Learning (AutoML), 2020 | 6 | 2020 |
| Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making D Taylor, S Spasov, P Liò ML4H: Machine Learning for Health (NeurIPS), 2019 | 5 | 2019 |
| Multimodal image fusion via deep generative models GM Dimitri, S Spasov, A Duggento, L Passamonti, P Lio’, N Toschi bioRxiv, 2021.03. 08.434427, 2021 | 2 | 2021 |
| Neuroevolve: A dynamic brain graph deep generative model SE Spasov, A Campbell, N Toschi, P Lio ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023 | 1 | 2023 |
| Dynamic Channel Execution: on-device Learning Method for Finding Compact Networks SE Spasov, P Liò EMC2: Workshop on Energy Efficient Machine Learning and Cognitive Computing …, 2019 | 1 | 2019 |
| DBGDGM: Dynamic Brain Graph Deep Generative Model A Campbell, S Spasov, N Toschi, P Lio Medical Imaging with Deep Learning (MIDL) 2023 (Oral), 2023 | | 2023 |
| Encoding parameter and structural efficiency in deep learning S Spasov | | 2022 |
| TG-DGM: Clustering Brain Activity using a Temporal Graph Deep Generative Mode SE Spasov, A Campbell, G Dimitri, A Di Stefano, F Scarselli, P Lio Medical Imaging with Deep Learning 2021, 2021 | | 2021 |
| Multimodal image fusion via deep generative models S Spasov, GM Dimitri | | |
| A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease within three years S Spasov, L Passamonti, A Duggento, P Liò, R Way, RM Roma | | |
Nicola ToschiDepartment of Biomedicine and Prevention, University of Rome Tor VergataVerified email at med.uniroma2.it
