Paolo Soda

0000-0003-2621-072X

Kontakt

paolo.soda@umu.se

Verksam som

Gästprofessor vid Institutionen för diagnostik och intervention

5A, Målpunkt H62, Norrlands universitetssjukhus Umeå universitet, 901 85 Umeå

Publikationer Publikationer

Agentic AI in agriculture: towards automatic farming systems

Complex, intelligent and software intensive systems: Proceedings of the 20th International conference on complex, intelligent, and software intensive systems (CISIS-2026), volume 2, Cham: Springer 2027 : 26-38

Bonifacio, Anna; Iele, Irene; Romoli, Giulia; et al.

A distributed deep learning architecture for cloud continuum deployment in a e-health scenario

Complex, intelligent and software intensive systems: Proceedings of the 20th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2026), Volume 2, Cham: Springer 2027 : 201-211

Sarcina, Francesco; Di Martino, Beniamino; Guarrasi, Valerio; et al.

Virtual scanning for NSCLC histology: investigating the discriminatory power of synthetic PET

ACM-BCB 2026 - 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Aksu, Fatih; Ciuffetti, Laura; Di Feola, Francesco; et al.

Context-Gated Cross-Modal Perception with Visual MAMBA for PET-CT Lung Tumor Segmentation

2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)

Ayllon, Elena Mulero; Shen, Linlin; Veltri, Pierangelo; et al.

Not another imputation method: a transformer-based model for missing values in tabular datasets

AI Open, Elsevier 2026, Vol. 7 : 96-122

Caruso, Camillo Maria; Soda, Paolo; Guarrasi, Valerio

Exploratory PET/CT radiomics for predicting early progression in locally advanced pancreatic cancer

Diagnostics, MDPI 2026, Vol. 16, (10)

Fiore, Michele; Cordelli, Ermanno; Petrianni, Gian Marco; et al.

Cross-dataset multivariate time-series model for Parkinson's diagnosis via keyboard dynamics

Artificial Intelligence for Biomedical Data: First International Workshop, AIBio 2025, Held in Conjunction with the European Conference on Artificial Intelligence, ECAI 2025, Bologna, Italy, October 25–26, 2025, Proceedings, Springer 2026 : 61-74

Francesconi, Arianna; Cappetta, Donato; Rebecchi, Fabio; et al.

Towards generalizable image classification models for detecting middle ear diseases

IEEE Access, IEEE 2026, Vol. 14 : 131655-131668

Lindmark, Manfred; Lundberg, Thorbjörn; Werner, Mimmi; et al.

Virtual Treatment for radiotherapy: Multimodal generative prediction of longitudinal NSCLC tumor progression

Computerized Medical Imaging and Graphics, Elsevier 2026, Vol. 132

Mantegna, Massimiliano; Ayllón, Elena Mulero; Caragliano, Alice Natalina; et al.

Can AI applied on MRI reliably predict shunt response in INPH?: A comprehensive exploration of deep learning and radiomics approaches using preoperative MRI

PLOS ONE, Public Library of Science (PLoS) 2026, Vol. 21, (6)

Mogensen, Klara; Guarrasi, Valerio; Behndig, Sofia; et al.

XGeM: a multi-prompt foundation model for multimodal medical data generation

Computerized Medical Imaging and Graphics, Elsevier 2026, Vol. 128

Molino, Daniele; Di Feola, Francesco; Faiella, Eliodoro; et al.

3D CT-to-PET translation via latent-guided contrastive alignment and brownian bridge diffusion

ACM-BCB 2026 - 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Mourya, Sarita; Di Feola, Francesco; Soda, Paolo

Predicting lung cancer survival with attention-based CT slices combination

Health Information Science and Systems, Springer Nature 2026, Vol. 14, (1)

Paolo, Domenico; Greco, Carlo; Ippolito, Edy; et al.

Predicting 2-year overall survival in NSCLC from CT scans using 2D CNNs and soft attention

Opening the personal gate between technology and health care: proceedings of MIE 2026, IOS Press 2026 : 113-117

Paolo, Domenico; Greco, Carlo; Ippolito, Edy; et al.

Multimodal clinical data integration for prognosis of pulmonary embolism: a comparative study

Computerized Medical Imaging and Graphics, Elsevier 2026, Vol. 133

Paolo, Domenico; Soda, Paolo; Tortora, Matteo; et al.

Machine learning models for sepsis: from early detection to short- and long-term prognosis

International Journal of Molecular Sciences, MDPI 2026, Vol. 27, (6)

Ristori, Maria Vittoria; Ruffini, Filippo; Spoto, Silvia; et al.

Augmented intelligence for multimodal virtual biopsy in breast cancer using generative artificial intelligence

Journal of Biomedical Informatics, Elsevier 2026, Vol. 174

Rofena, Aurora; Piccolo, Claudia Lucia; Zobel, Bruno Beomonte; et al.

Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging

Computer Methods and Programs in Biomedicine, Elsevier 2026, Vol. 275

Ruffini, Filippo; Ayllón, Elena Mulero; Shen, Linlin; et al.

Concept-enhanced multimodal RAG: towards interpretable and accurate radiology report generation

Information Systems Frontiers

Salmè, Marco; Siciliano, Federico; Silvestri, Fabrizio; et al.

Beyond the Generative Learning Trilemma: generative model assessment in data scarcity domains

IEEE Access, Institute of Electrical and Electronics Engineers (IEEE) 2026, Vol. 14 : 117679-117696

Salmè, Marco; Tronchin, Lorenzo; Sicilia, Rosa; et al.

[¹⁸F]fdg PET/CT radiomics for predicting pathological risk subtypes of thymic epithelial tumors: a bicentric study

Cancers, MDPI 2026, Vol. 18, (13)

Sarubbi, Antonio; Frasca, Luca; Aksu, Fatih; et al.

MATNet: multi-level fusion transformer-based model for day-ahead PV generation forecasting

Frontiers in Artificial Intelligence, Frontiers Media S.A. 2026, Vol. 9

Tortora, Matteo; Conte, Francesco; Natrella, Gianluca; et al.

Beyond a single mode: gan ensembles for diverse medical data generation

Computer Methods and Programs in Biomedicine, Elsevier 2026, Vol. 277

Tronchin, Lorenzo; Löfstedt, Tommy; Soda, Paolo; et al.

ACGM: attribute-centric graph modeling network for concurrent missing tabular data imputation and COVID-19 prognosis

IEEE journal of biomedical and health informatics, IEEE 2026, Vol. 30, (4) : 3307-3320

Wu, Zhuoru; Chen, Wenting; Li, Xuechen; et al.

Enhancing NSCLC histological subtype classification: a federated learning approach using triplet loss

Pattern Recognition. ICPR 2024 International Workshops and Challenges: Kolkata, India, December 1, 2024, Proceedings, Part II, Springer Nature 2025 : 154-168

Aksu, Fatih; Cordelli, Ermanno; Gelardi, Fabrizia; et al.

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

Pattern Recognition Letters, Elsevier 2025, Vol. 193 : 86-93

Aksu, Fatih; Gelardi, Fabrizia; Chiti, Arturo; et al.

NSCLC histological subtype classification from CT scans using generalist 3D medical foundation models

Proceedings - 2025 IEEE 13th International Conference on Healthcare Informatics, ICHI 2025: proceedings, Institute of Electrical and Electronics Engineers (IEEE) 2025 : 648-653

Aksu, Fatih; Gelardi, Fabrizia; Chiti, Arturo; et al.

Can foundation models really segment tumors?: A benchmarking odyssey in lung CT imaging

2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), Institute of Electrical and Electronics Engineers (IEEE) 2025 : 375-380

Ayllón, Elena Mulero; Mantegna, Massimiliano; Shen, Linlin; et al.

Generative adversarial networks for synthetic longitudinal electronic health records enabling cardiovascular digital twins

2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), Institute of Electrical and Electronics Engineers (IEEE) 2025 : 25-28

Bertgren, Amanda; Öhberg, Fredrik; Soda, Paolo; et al.

Doctor-in-the-Loop: An explainable, multi-view deep learning framework for predicting pathological response in non-small cell lung cancer

Image and Vision Computing, Elsevier 2025, Vol. 161

Caragliano, Alice Natalina; Ruffini, Filippo; Greco, Carlo; et al.

Multimodal doctor-in-the-loop: a clinically-guided explainable framework for predicting pathological response in non-small cell lung cancer

Proceedings of the international joint conference on neural networks

Caragliano, Alice Natalina; Tacconi, Claudia; Greco, Carlo; et al.

MARIA: a multimodal transformer model for incomplete healthcare data

Computers in Biology and Medicine, Elsevier 2025, Vol. 196

Caruso, Camillo Maria; Soda, Paolo; Guarrasi, Valerio

Long-term outcomes from pembrolizumab monotherapy in patients with advanced NSCLC, PD-L1 expression ≥ 50 %, and poor performance status: transformer-based AI to characterize prognostic complexity

Lung Cancer, Elsevier 2025, Vol. 209

Cortellini, Alessio; Garbo, Edoardo; La Cava, Giulia; et al.

Transformer-based AI approach to unravel long-term, time-dependent prognostic complexity in patients with advanced NSCLC and PD-L1 ≥50%: insights from the pembrolizumab 5-year global registry

Journal for ImmunoTherapy of Cancer, BMJ Publishing Group Ltd 2025, Vol. 13, (9)

Cortellini, Alessio; Santo, Valentina; Brunetti, Leonardo; et al.

Deep learning for human locomotion analysis in lower-limb exoskeletons: a comparative study

Frontiers in Computer Science, Frontiers Media S.A. 2025, Vol. 7

Coser, Omar; Tamantini, Christian; Tortora, Matteo; et al.

Hybrid 3D CNN-MAMBA for emphysema classification in the SCAPIS cohort

2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), Institute of Electrical and Electronics Engineers (IEEE) 2025 : 686-692

Di Feola, Francesco; De Maria, Marida; Bergström, Göran; et al.

Texture-aware StarGAN for CT data harmonization

Proceedings of the international joint conference on neural networks

Di Feola, Francesco; Pompilio, Ludovica; Assolito, Cecilia; et al.

Multi-scale texture loss for CT denoising with GANs

AI Open, Elsevier 2025, Vol. 6 : 142-154

Di Feola, Francesco; Tronchin, Lorenzo; Guarrasi, Valerio; et al.

Class balancing diversity multimodal ensemble for Alzheimer's disease diagnosis and early detection

Computerized Medical Imaging and Graphics, Elsevier 2025, Vol. 123

Francesconi, Arianna; di Biase, Lazzaro; Cappetta, Donato; et al.

Identifying cardiogenic shock sub-phenotypes with machine learning: a multicenter study combining clinical and echocardiographic data

JACC: Advances, Elsevier 2025, Vol. 4, (11)

Ghionzoli, Nicolò; Stefanini, Andrea; Halasz, Geza; et al.

A systematic review of intermediate fusion in multimodal deep learning for biomedical applications

Image and Vision Computing, Elsevier 2025, Vol. 158

Guarrasi, Valerio; Aksu, Fatih; Caruso, Camillo Maria; et al.

Beyond unimodal analysis: multimodal ensemble learning for enhanced assessment of atherosclerotic disease progression

Computerized Medical Imaging and Graphics, Elsevier 2025, Vol. 124

Guarrasi, Valerio; Bertgren, Amanda; Näslund, Ulf; et al.

Medicine without boundaries: generative AI for translating medical data across modalities

Ital-IA-TW 2025: Joint Proceedings of the Thematic Workshops at Ital-IA 2025colocated with the 5th National Conference on Artificial Intelligence, organized by CINI (Ital-IA 2025)

Guarrasi, Valerio; Di Feola, Francesco; Iannello, Giulio; et al.

Whole-body image-to-image translation for a virtual scanner in a healthcare digital twin

2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), Institute of Electrical and Electronics Engineers (IEEE) 2025 : 528-534

Guarrasi, Valerio; Di Feola, Francesco; Restivo, Rebecca; et al.

Timing is everything: finding the optimal fusion points in multimodal medical imaging

Proceedings of the International Joint Conference on Neural Networks

Guarrasi, Valerio; Mogensen, Klara; Tassinari, Sara; et al.

Benchmarking GAN-based vs classical data augmentation on biomedical images

Pattern Recognition. ICPR 2024 International Workshops and Challenges: Kolkata, India, December 1, 2024, Proceedings, Part II, Springer Science and Business Media Deutschland GmbH 2025 : 92-104

Mantegna, Massimiliano; Tronchin, Lorenzo; Tortora, Matteo; et al.

An optimized ensemble search approach for classification of higher-level gait disorder using brain magnetic resonance images

Computers in Biology and Medicine, Elsevier 2025, Vol. 184

Mogensen, Klara; Guarrasi, Valerio; Larsson, Jenny; et al.

Any-to-any vision-language model for multimodal x-ray imaging and radiological report generation

Proceedings of the International Joint Conference on Neural Networks

Molino, Daniele; Di Feola, Francesco; Shen, Linlin; et al.

Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs

BMC Medical Informatics and Decision Making, Springer Nature 2025, Vol. 25, (1)

Paolo, Domenico; Greco, Carlo; Cortellini, Alessio; et al.

Pathologic complete response prediction with machine learning using hierarchical attention feature extraction

Pattern Recognition. ICPR 2024 International Workshops and Challenges: Kolkata, India, December 1, 2024, Proceedings, Part II, Springer Nature 2025 : 255-267

Paolo, Domenico; Russo, Ciro; Russo, Giulio; et al.

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