A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. - Simeon E. Spasov; Luca Passamonti; Andrea Duggento; Pietro Liò; Nicola Toschi (EMBC, 2018)

Dynamic Neural Network Channel Execution for Efficient Training. - Simeon E. Spasov; Pietro Liò (BMVC, 2019)

Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making. - Devin Taylor; Simeon E. Spasov; Pietro Liò (CoRR, 2019)

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. - Simeon E. Spasov; Luca Passamonti; Andrea Duggento; Pietro Liò; Nicola Toschi (NeuroImage, 2019)

Unsupervised stratification in neuroimaging through deep latent embeddings. - Giovanna Maria Dimitri; Simeon E. Spasov; Andrea Duggento; Luca Passamonti; Pietro Lio'; Nicola Toschi (EMBC, 2020)

RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow. - Samuel Glass; Simeon E. Spasov; Pietro Liò (CoRR, 2020)

GRADE: Graph Dynamic Embedding. - Simeon E. Spasov; Alessandro Di Stefano; Pietro Liò; Jian Tang 005 (CoRR, 2020)

Multimodal and multicontrast image fusion via deep generative models. - Giovanna Maria Dimitri; Simeon E. Spasov; Andrea Duggento; Luca Passamonti; Pietro Lió; Nicola Toschi (Inf. Fusion, 2022)

DBGDGM: Dynamic Brain Graph Deep Generative Model. - Alexander Campbell; Simeon Emilov Spasov; Nicola Toschi; Pietro Lio (MIDL, 2023)

Contrastive learning for unsupervised medical image clustering and reconstruction. - Matteo Ferrante; Tommaso Boccato; Andrea Duggento; Simeon E. Spasov; Nicola Toschi (NLDL, 2023)

VAESim: A probabilistic approach for self-supervised prototype discovery. - Matteo Ferrante; Tommaso Boccato; Simeon E. Spasov; Andrea Duggento; Nicola Toschi (Image Vis. Comput., 2023)